diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index fb524f92..4aeca375 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -29,10 +29,6 @@ jobs: run: | poetry run python benchmarks/overall.py benchmark_data/pdfs benchmark_data/references report.json poetry run python scripts/verify_benchmark_scores.py report.json --type marker - - name: Run table benchmark - run: | - poetry run python benchmarks/table.py tables.json - poetry run python scripts/verify_benchmark_scores.py tables.json --type table diff --git a/.gitignore b/.gitignore index f4cf5dcf..933a116e 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,7 @@ wandb *.dat report.json benchmark_data +debug_data # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/CLA.md b/CLA.md index d80b275d..296e7fa1 100644 --- a/CLA.md +++ b/CLA.md @@ -1,6 +1,6 @@ Marker Contributor Agreement -This Marker Contributor Agreement ("MCA") applies to any contribution that you make to any product or project managed by us (the "project"), and sets out the intellectual property rights you grant to us in the contributed materials. The term "us" shall mean Vikas Paruchuri. The term "you" shall mean the person or entity identified below. +This Marker Contributor Agreement ("MCA") applies to any contribution that you make to any product or project managed by us (the "project"), and sets out the intellectual property rights you grant to us in the contributed materials. The term "us" shall mean Endless Labs, Inc. The term "you" shall mean the person or entity identified below. If you agree to be bound by these terms, sign by writing "I have read the CLA document and I hereby sign the CLA" in response to the CLA bot Github comment. Read this agreement carefully before signing. These terms and conditions constitute a binding legal agreement. @@ -20,5 +20,5 @@ If you or your affiliates institute patent litigation against any entity (includ - each contribution that you submit is and shall be an original work of authorship and you can legally grant the rights set out in this MCA; - to the best of your knowledge, each contribution will not violate any third party's copyrights, trademarks, patents, or other intellectual property rights; and - each contribution shall be in compliance with U.S. export control laws and other applicable export and import laws. -You agree to notify us if you become aware of any circumstance which would make any of the foregoing representations inaccurate in any respect. Vikas Paruchuri may publicly disclose your participation in the project, including the fact that you have signed the MCA. +You agree to notify us if you become aware of any circumstance which would make any of the foregoing representations inaccurate in any respect. Endless Labs, Inc. may publicly disclose your participation in the project, including the fact that you have signed the MCA. 6. This MCA is governed by the laws of the State of California and applicable U.S. Federal law. Any choice of law rules will not apply. \ No newline at end of file diff --git a/README.md b/README.md index a546b633..2022173b 100644 --- a/README.md +++ b/README.md @@ -42,7 +42,7 @@ See [below](#benchmarks) for detailed speed and accuracy benchmarks, and instruc I want marker to be as widely accessible as possible, while still funding my development/training costs. Research and personal usage is always okay, but there are some restrictions on commercial usage. -The weights for the models are licensed `cc-by-nc-sa-4.0`, but I will waive that for any organization under $5M USD in gross revenue in the most recent 12-month period AND under $5M in lifetime VC/angel funding raised. If you want to remove the GPL license requirements (dual-license) and/or use the weights commercially over the revenue limit, check out the options [here](https://www.datalab.to). +The weights for the models are licensed `cc-by-nc-sa-4.0`, but I will waive that for any organization under $5M USD in gross revenue in the most recent 12-month period AND under $5M in lifetime VC/angel funding raised. You also must not be competitive with the [Datalab API](https://www.datalab.to/). If you want to remove the GPL license requirements (dual-license) and/or use the weights commercially over the revenue limit, check out the options [here](https://www.datalab.to). # Hosted API @@ -89,6 +89,7 @@ First, some configuration: - Inspect the settings in `marker/settings.py`. You can override any settings with environment variables. - Your torch device will be automatically detected, but you can override this. For example, `TORCH_DEVICE=cuda`. - By default, marker will use `surya` for OCR. Surya is slower on CPU, but more accurate than tesseract. It also doesn't require you to specify the languages in the document. If you want faster OCR, set `OCR_ENGINE` to `ocrmypdf`. This also requires external dependencies (see above). If you don't want OCR at all, set `OCR_ENGINE` to `None`. +- Some PDFs, even digital ones, have bad text in them. Set `OCR_ALL_PAGES=true` to force OCR if you find bad output from marker. ## Interactive App @@ -107,15 +108,15 @@ marker_single /path/to/file.pdf /path/to/output/folder --batch_multiplier 2 --ma - `--batch_multiplier` is how much to multiply default batch sizes by if you have extra VRAM. Higher numbers will take more VRAM, but process faster. Set to 2 by default. The default batch sizes will take ~3GB of VRAM. - `--max_pages` is the maximum number of pages to process. Omit this to convert the entire document. +- `--start_page` is the page to start from (default is None, will start from the first page). - `--langs` is an optional comma separated list of the languages in the document, for OCR. Optional by default, required if you use tesseract. -- `--ocr_all_pages` is an optional argument to force OCR on all pages of the PDF. If this or the env var `OCR_ALL_PAGES` are true, OCR will be forced. The list of supported languages for surya OCR is [here](https://github.com/VikParuchuri/surya/blob/master/surya/languages.py). If you need more languages, you can use any language supported by [Tesseract](https://tesseract-ocr.github.io/tessdoc/Data-Files#data-files-for-version-400-november-29-2016) if you set `OCR_ENGINE` to `ocrmypdf`. If you don't need OCR, marker can work with any language. ## Convert multiple files ```shell -marker /path/to/input/folder /path/to/output/folder --workers 4 --max 10 --min_length 10000 +marker /path/to/input/folder /path/to/output/folder --workers 4 --max 10 ``` - `--workers` is the number of pdfs to convert at once. This is set to 1 by default, but you can increase it to increase throughput, at the cost of more CPU/GPU usage. Marker will use 5GB of VRAM per worker at the peak, and 3.5GB average. @@ -136,7 +137,7 @@ You can use language names or codes. The exact codes depend on the OCR engine. ## Convert multiple files on multiple GPUs ```shell -MIN_LENGTH=10000 METADATA_FILE=../pdf_meta.json NUM_DEVICES=4 NUM_WORKERS=15 marker_chunk_convert ../pdf_in ../md_out +METADATA_FILE=../pdf_meta.json NUM_DEVICES=4 NUM_WORKERS=15 marker_chunk_convert ../pdf_in ../md_out ``` - `METADATA_FILE` is an optional path to a json file with metadata about the pdfs. See above for the format. @@ -146,19 +147,52 @@ MIN_LENGTH=10000 METADATA_FILE=../pdf_meta.json NUM_DEVICES=4 NUM_WORKERS=15 mar Note that the env variables above are specific to this script, and cannot be set in `local.env`. +# Output format + +The output will be a markdown file, but there will also be a metadata json file that gives information about the conversion process. It has these fields: + +```json +{ + "languages": null, // any languages that were passed in + "filetype": "pdf", // type of the file + "pdf_toc": [], // the table of contents from the pdf + "computed_toc": [], //the computed table of contents + "pages": 10, // page count + "ocr_stats": { + "ocr_pages": 0, // number of pages OCRed + "ocr_failed": 0, // number of pages where OCR failed + "ocr_success": 0, + "ocr_engine": "none" + }, + "block_stats": { + "header_footer": 0, + "code": 0, // number of code blocks + "table": 2, // number of tables + "equations": { + "successful_ocr": 0, + "unsuccessful_ocr": 0, + "equations": 0 + } + } +} +``` + # Troubleshooting There are some settings that you may find useful if things aren't working the way you expect: -- `OCR_ALL_PAGES` - set this to true to force OCR all pages. This can be very useful if the table layouts aren't recognized properly by default, or if there is garbled text. +- `OCR_ALL_PAGES` - set this to true to force OCR all pages. This can be very useful if there is garbled text in the output of marker. - `TORCH_DEVICE` - set this to force marker to use a given torch device for inference. - `OCR_ENGINE` - can set this to `surya` or `ocrmypdf`. -- `DEBUG` - setting this to `True` shows ray logs when converting multiple pdfs - Verify that you set the languages correctly, or passed in a metadata file. - If you're getting out of memory errors, decrease worker count (increased the `VRAM_PER_TASK` setting). You can also try splitting up long PDFs into multiple files. In general, if output is not what you expect, trying to OCR the PDF is a good first step. Not all PDFs have good text/bboxes embedded in them. +## Debugging + +Set `DEBUG=true` to save data to the `debug` subfolder in the marker root directory. This will save images of each page with detected layout and text, as well as output a json file with additional bounding box information. + ## Useful settings These settings can improve/change output quality: @@ -210,21 +244,13 @@ poetry install Download the benchmark data [here](https://drive.google.com/file/d/1ZSeWDo2g1y0BRLT7KnbmytV2bjWARWba/view?usp=sharing) and unzip. Then run the overall benchmark like this: ```shell -python benchmark/overall.py data/pdfs data/references report.json --nougat +python benchmarks/overall.py data/pdfs data/references report.json --nougat ``` This will benchmark marker against other text extraction methods. It sets up batch sizes for nougat and marker to use a similar amount of GPU RAM for each. Omit `--nougat` to exclude nougat from the benchmark. I don't recommend running nougat on CPU, since it is very slow. -### Table benchmark - -There is a benchmark for table parsing, which you can run with: - -```shell -python benchmarks/table.py test_data/tables.json -``` - # Thanks This work would not have been possible without amazing open source models and datasets, including (but not limited to): @@ -233,6 +259,5 @@ This work would not have been possible without amazing open source models and da - Texify - Pypdfium2/pdfium - DocLayNet from IBM -- ByT5 from Google Thank you to the authors of these models and datasets for making them available to the community! \ No newline at end of file diff --git a/benchmarks/overall.py b/benchmarks/overall.py index ecb67c02..2db392b6 100644 --- a/benchmarks/overall.py +++ b/benchmarks/overall.py @@ -48,7 +48,7 @@ def nougat_prediction(pdf_filename, batch_size=1): def main(): - parser = argparse.ArgumentParser(description="Benchmark PDF to MD conversion. Needs source pdfs, and a refernece folder with the correct markdown.") + parser = argparse.ArgumentParser(description="Benchmark PDF to MD conversion. Needs source pdfs, and a reference folder with the correct markdown.") parser.add_argument("in_folder", help="Input PDF files") parser.add_argument("reference_folder", help="Reference folder with reference markdown files") parser.add_argument("out_file", help="Output filename") diff --git a/benchmarks/table.py b/benchmarks/table.py deleted file mode 100644 index 45cd3888..00000000 --- a/benchmarks/table.py +++ /dev/null @@ -1,77 +0,0 @@ -import argparse -import json - -import datasets -from surya.schema import LayoutResult, LayoutBox -from tqdm import tqdm - -from marker.benchmark.table import score_table -from marker.schema.bbox import rescale_bbox -from marker.schema.page import Page -from marker.tables.table import format_tables - - - -def main(): - parser = argparse.ArgumentParser(description="Benchmark table conversion.") - parser.add_argument("out_file", help="Output filename for results") - parser.add_argument("--dataset", type=str, help="Dataset to use", default="vikp/table_bench") - args = parser.parse_args() - - ds = datasets.load_dataset(args.dataset, split="train") - - results = [] - for i in tqdm(range(len(ds)), desc="Evaluating tables"): - row = ds[i] - marker_page = Page(**json.loads(row["marker_page"])) - table_bbox = row["table_bbox"] - gpt4_table = json.loads(row["gpt_4_table"])["markdown_table"] - - # Counterclockwise polygon from top left - table_poly = [ - [table_bbox[0], table_bbox[1]], - [table_bbox[2], table_bbox[1]], - [table_bbox[2], table_bbox[3]], - [table_bbox[0], table_bbox[3]], - ] - - # Remove all other tables from the layout results - layout_result = LayoutResult( - bboxes=[ - LayoutBox( - label="Table", - polygon=table_poly - ) - ], - segmentation_map="", - image_bbox=marker_page.text_lines.image_bbox - ) - - marker_page.layout = layout_result - format_tables([marker_page]) - - table_blocks = [block for block in marker_page.blocks if block.block_type == "Table"] - if len(table_blocks) != 1: - continue - - table_block = table_blocks[0] - table_md = table_block.lines[0].spans[0].text - - results.append({ - "score": score_table(table_md, gpt4_table), - "arxiv_id": row["arxiv_id"], - "page_idx": row["page_idx"], - "marker_table": table_md, - "gpt4_table": gpt4_table, - "table_bbox": table_bbox - }) - - avg_score = sum([r["score"] for r in results]) / len(results) - print(f"Evaluated {len(results)} tables, average score is {avg_score}.") - - with open(args.out_file, "w+") as f: - json.dump(results, f, indent=2) - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/convert_single.py b/convert_single.py index eb99bf1d..f668ce7b 100755 --- a/convert_single.py +++ b/convert_single.py @@ -2,6 +2,7 @@ import pypdfium2 # Needs to be at the top to avoid warnings import os + os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" # For some reason, transformers decided to use .isin for a simple op, which is not supported on MPS import argparse @@ -22,8 +23,6 @@ def main(): parser.add_argument("--start_page", type=int, default=None, help="Page to start processing at") parser.add_argument("--langs", type=str, help="Optional languages to use for OCR, comma separated", default=None) parser.add_argument("--batch_multiplier", type=int, default=2, help="How much to increase batch sizes") - parser.add_argument("--debug", action="store_true", help="Enable debug logging", default=False) - parser.add_argument("--ocr_all_pages", action="store_true", help="Force OCR on all pages", default=False) args = parser.parse_args() langs = args.langs.split(",") if args.langs else None @@ -31,14 +30,13 @@ def main(): fname = args.filename model_lst = load_all_models() start = time.time() - full_text, images, out_meta = convert_single_pdf(fname, model_lst, max_pages=args.max_pages, langs=langs, batch_multiplier=args.batch_multiplier, start_page=args.start_page, ocr_all_pages=args.ocr_all_pages) + full_text, images, out_meta = convert_single_pdf(fname, model_lst, max_pages=args.max_pages, langs=langs, batch_multiplier=args.batch_multiplier, start_page=args.start_page) fname = os.path.basename(fname) subfolder_path = save_markdown(args.output, fname, full_text, images, out_meta) print(f"Saved markdown to the {subfolder_path} folder") - if args.debug: - print(f"Total time: {time.time() - start}") + print(f"Total time: {time.time() - start}") if __name__ == "__main__": diff --git a/marker/cleaners/headings.py b/marker/cleaners/headings.py index 6efb4b0e..8be2bfa8 100644 --- a/marker/cleaners/headings.py +++ b/marker/cleaners/headings.py @@ -1,4 +1,7 @@ +from collections import defaultdict from typing import List +import numpy as np +from sklearn.cluster import KMeans from marker.settings import settings from marker.schema.bbox import rescale_bbox @@ -57,3 +60,67 @@ def split_heading_blocks(pages: List[Page]): new_blocks.append(copied_block) page.blocks = new_blocks + + +def bucket_headings(line_heights, num_levels=settings.HEADING_LEVEL_COUNT): + if len(line_heights) <= num_levels: + return [] + + data = np.asarray(line_heights).reshape(-1, 1) + labels = KMeans(n_clusters=num_levels, random_state=0, n_init="auto").fit_predict(data) + data_labels = np.concatenate([data, labels.reshape(-1, 1)], axis=1) + data_labels = np.sort(data_labels, axis=0) + + cluster_means = {label: np.mean(data_labels[data_labels[:, 1] == label, 0]) for label in np.unique(labels)} + label_max = None + label_min = None + heading_ranges = [] + prev_cluster = None + for row in data_labels: + value, label = row + if prev_cluster is not None and label != prev_cluster: + prev_cluster_mean = cluster_means[prev_cluster] + cluster_mean = cluster_means[label] + if cluster_mean * settings.HEADING_MERGE_THRESHOLD < prev_cluster_mean: + heading_ranges.append((label_min, label_max)) + label_min = None + label_max = None + + label_min = value if label_min is None else min(label_min, value) + label_max = value if label_max is None else max(label_max, value) + prev_cluster = label + + if label_min is not None: + heading_ranges.append((label_min, label_max)) + + heading_ranges = sorted(heading_ranges, key=lambda x: x[0], reverse=True) + + return heading_ranges + + +def infer_heading_levels(pages: List[Page], height_tol=.99): + all_line_heights = [] + for page in pages: + for block in page.blocks: + if block.block_type not in ["Title", "Section-header"]: + continue + + all_line_heights.extend([l.height for l in block.lines]) + + heading_ranges = bucket_headings(all_line_heights) + + for page in pages: + for block in page.blocks: + if block.block_type not in ["Title", "Section-header"]: + continue + + block_heights = [l.height for l in block.lines] # Account for rotation + avg_height = sum(block_heights) / len(block_heights) + for idx, (min_height, max_height) in enumerate(heading_ranges): + if avg_height >= min_height * height_tol: + block.heading_level = idx + 1 + break + + if block.heading_level is None: + block.heading_level = settings.HEADING_DEFAULT_LEVEL + diff --git a/marker/cleaners/toc.py b/marker/cleaners/toc.py new file mode 100644 index 00000000..3e33a45a --- /dev/null +++ b/marker/cleaners/toc.py @@ -0,0 +1,29 @@ +from typing import List + +from marker.schema.page import Page + + +def get_pdf_toc(doc, max_depth=15): + toc = doc.get_toc(max_depth=max_depth) + toc_list = [] + for item in toc: + list_item = { + "title": item.title, + "level": item.level, + "page": item.page_index, + } + toc_list.append(list_item) + return toc_list + + +def compute_toc(pages: List[Page]): + toc = [] + for page in pages: + for block in page.blocks: + if block.block_type in ["Title", "Section-header"]: + toc.append({ + "title": block.prelim_text, + "level": block.heading_level, + "page": page.pnum + }) + return toc diff --git a/marker/convert.py b/marker/convert.py index 3c56fc9c..14510fab 100644 --- a/marker/convert.py +++ b/marker/convert.py @@ -10,7 +10,7 @@ from marker.utils import flush_cuda_memory from marker.tables.table import format_tables -from marker.debug.data import dump_bbox_debug_data +from marker.debug.data import dump_bbox_debug_data, draw_page_debug_images from marker.layout.layout import surya_layout, annotate_block_types from marker.layout.order import surya_order, sort_blocks_in_reading_order from marker.ocr.lang import replace_langs_with_codes, validate_langs @@ -20,15 +20,15 @@ from marker.cleaners.headers import filter_header_footer, filter_common_titles from marker.equations.equations import replace_equations from marker.pdf.utils import find_filetype -from marker.postprocessors.editor import edit_full_text from marker.cleaners.code import identify_code_blocks, indent_blocks from marker.cleaners.bullets import replace_bullets -from marker.cleaners.headings import split_heading_blocks +from marker.cleaners.headings import split_heading_blocks, infer_heading_levels from marker.cleaners.fontstyle import find_bold_italic from marker.postprocessors.markdown import merge_spans, merge_lines, get_full_text from marker.cleaners.text import cleanup_text from marker.images.extract import extract_images from marker.images.save import images_to_dict +from marker.cleaners.toc import compute_toc from typing import List, Dict, Tuple, Optional from marker.settings import settings @@ -73,7 +73,7 @@ def convert_single_pdf( start_page=start_page ) out_meta.update({ - "toc": toc, + "pdf_toc": toc, "pages": len(pages), }) @@ -83,7 +83,7 @@ def convert_single_pdf( doc.del_page(0) # Unpack models from list - texify_model, layout_model, order_model, edit_model, detection_model, ocr_model = model_lst + texify_model, layout_model, order_model, detection_model, ocr_model, table_rec_model = model_lst # Identify text lines on pages surya_detection(doc, pages, detection_model, batch_multiplier=batch_multiplier) @@ -108,22 +108,23 @@ def convert_single_pdf( # Add block types in annotate_block_types(pages) - # Dump debug data if flags are set - dump_bbox_debug_data(doc, fname, pages) - # Find reading order for blocks # Sort blocks by reading order surya_order(doc, pages, order_model, batch_multiplier=batch_multiplier) sort_blocks_in_reading_order(pages) flush_cuda_memory() + # Dump debug data if flags are set + draw_page_debug_images(fname, pages) + dump_bbox_debug_data(fname, pages) + # Fix code blocks code_block_count = identify_code_blocks(pages) out_meta["block_stats"]["code"] = code_block_count indent_blocks(pages) # Fix table blocks - table_count = format_tables(pages) + table_count = format_tables(pages, doc, fname, detection_model, table_rec_model, ocr_model) out_meta["block_stats"]["table"] = table_count for page in pages: @@ -146,8 +147,12 @@ def convert_single_pdf( # Split out headers split_heading_blocks(pages) + infer_heading_levels(pages) find_bold_italic(pages) + # Use headers to compute a table of contents + out_meta["computed_toc"] = compute_toc(pages) + # Copy to avoid changing original data merged_lines = merge_spans(filtered) text_blocks = merge_lines(merged_lines) @@ -160,14 +165,6 @@ def convert_single_pdf( # Replace bullet characters with a - full_text = replace_bullets(full_text) - # Postprocess text with editor model - full_text, edit_stats = edit_full_text( - full_text, - edit_model, - batch_multiplier=batch_multiplier - ) - flush_cuda_memory() - out_meta["postprocess_stats"] = {"edit": edit_stats} doc_images = images_to_dict(pages) return full_text, doc_images, out_meta \ No newline at end of file diff --git a/marker/debug/data.py b/marker/debug/data.py index 0eb3cefe..c094981e 100644 --- a/marker/debug/data.py +++ b/marker/debug/data.py @@ -1,76 +1,109 @@ -import base64 import json +import math import os from typing import List -from marker.pdf.images import render_image +from marker.debug.render import render_on_image +from marker.schema.bbox import rescale_bbox from marker.schema.page import Page from marker.settings import settings from PIL import Image -import io -def dump_equation_debug_data(doc, images, converted_spans): - if not settings.DEBUG_DATA_FOLDER or settings.DEBUG_LEVEL == 0: - return +def draw_layout_page_debug_images(fname, pages: List[Page]): + # Remove extension from doc name + doc_base = os.path.basename(fname).rsplit(".", 1)[0] - if len(images) == 0: - return + debug_folder = os.path.join(settings.DEBUG_DATA_FOLDER, doc_base) + os.makedirs(debug_folder, exist_ok=True) + for idx, page in enumerate(pages): + img_size = (int(math.ceil(page.text_lines.image_bbox[2])), int(math.ceil(page.text_lines.image_bbox[3]))) + png_image = Image.new("RGB", img_size, color="white") + + line_bboxes = [] + line_text = [] + for block in page.blocks: + for line in block.lines: + line_bboxes.append(rescale_bbox(page.bbox, page.text_lines.image_bbox, line.bbox)) + line_text.append(line.prelim_text) + + render_on_image(line_bboxes, png_image, labels=line_text, color="black", draw_bbox=False) + pdf_image = png_image.copy() + + line_bboxes = [line.bbox for line in page.text_lines.bboxes] + render_on_image(line_bboxes, png_image, color="blue") + + layout_boxes = [rescale_bbox(page.layout.image_bbox, page.text_lines.image_bbox, box.bbox) for box in page.layout.bboxes] + layout_labels = [box.label for box in page.layout.bboxes] + + render_on_image(layout_boxes, png_image, labels=layout_labels, color="red") + + order_labels = [str(i) for i in range(len(page.layout.bboxes))] + render_on_image(layout_boxes, png_image, labels=order_labels, color="green", draw_bbox=False, label_offset=5) + + debug_file = os.path.join(debug_folder, f"layout_page_{idx}.png") + png_image.save(debug_file) + + # PDF Image + + block_bboxes = [rescale_bbox(page.bbox, page.text_lines.image_bbox, block.bbox) for block in page.blocks] + block_labels = [block.block_type for block in page.blocks] + render_on_image(block_bboxes, pdf_image, labels=block_labels, color="red") - # We attempted one conversion per image - assert len(converted_spans) == len(images) - - data_lines = [] - for idx, (pil_image, converted_span) in enumerate(zip(images, converted_spans)): - if converted_span is None: - continue - # Image is a BytesIO object - img_bytes = io.BytesIO() - pil_image.save(img_bytes, format="WEBP", lossless=True) - b64_image = base64.b64encode(img_bytes.getvalue()).decode("utf-8") - data_lines.append({ - "image": b64_image, - "text": converted_span.text, - "bbox": converted_span.bbox - }) + block_order = [str(i) for i in range(len(page.blocks))] + render_on_image(block_bboxes, pdf_image, labels=block_order, color="green", draw_bbox=False, label_offset=5) + debug_file = os.path.join(debug_folder, f"pdf_page_{idx}.png") + pdf_image.save(debug_file) + + +def draw_pdf_page_debug_images(fname, pages: List[Page]): # Remove extension from doc name - doc_base = os.path.basename(doc.name).rsplit(".", 1)[0] + doc_base = os.path.basename(fname).rsplit(".", 1)[0] + + debug_folder = os.path.join(settings.DEBUG_DATA_FOLDER, doc_base) + os.makedirs(debug_folder, exist_ok=True) + for idx, page in enumerate(pages): + img_size = (int(math.ceil(page.text_lines.image_bbox[2])), int(math.ceil(page.text_lines.image_bbox[3]))) + png_image = Image.new("RGB", img_size, color="white") + + line_bboxes = [] + line_text = [] + for block in page.blocks: + for line in block.lines: + line_bboxes.append(rescale_bbox(page.bbox, page.text_lines.image_bbox, line.bbox)) + line_text.append(line.prelim_text) + + + + +def draw_page_debug_images(fname, pages: List[Page]): + if not settings.DEBUG: + return + + draw_layout_page_debug_images(fname, pages) + draw_pdf_page_debug_images(fname, pages) - debug_file = os.path.join(settings.DEBUG_DATA_FOLDER, f"{doc_base}_equations.json") - with open(debug_file, "w+") as f: - json.dump(data_lines, f) -def dump_bbox_debug_data(doc, fname, blocks: List[Page]): - if not settings.DEBUG_DATA_FOLDER or settings.DEBUG_LEVEL < 2: +def dump_bbox_debug_data(fname, pages: List[Page]): + if not settings.DEBUG: return # Remove extension from doc name - doc_base = fname.rsplit(".", 1)[0] + doc_base = os.path.basename(fname).rsplit(".", 1)[0] debug_file = os.path.join(settings.DEBUG_DATA_FOLDER, f"{doc_base}_bbox.json") debug_data = [] - for idx, page_blocks in enumerate(blocks): - page = doc[idx] - - png_image = render_image(page, dpi=settings.TEXIFY_DPI) - width, height = png_image.size - max_dimension = 6000 - if width > max_dimension or height > max_dimension: - scaling_factor = min(max_dimension / width, max_dimension / height) - png_image = png_image.resize((int(width * scaling_factor), int(height * scaling_factor)), Image.ANTIALIAS) - - img_bytes = io.BytesIO() - png_image.save(img_bytes, format="WEBP", lossless=True, quality=100) - b64_image = base64.b64encode(img_bytes.getvalue()).decode("utf-8") - - page_data = page_blocks.model_dump() - page_data["image"] = b64_image + for idx, page_blocks in enumerate(pages): + page_data = page_blocks.model_dump(exclude=["images", "layout", "text_lines"]) + page_data["layout"] = page_blocks.layout.model_dump(exclude=["segmentation_map"]) + page_data["text_lines"] = page_blocks.text_lines.model_dump(exclude=["heatmap", "affinity_map"]) debug_data.append(page_data) with open(debug_file, "w+") as f: json.dump(debug_data, f) + print(f"Dumped bbox debug data to {debug_file}") diff --git a/marker/debug/render.py b/marker/debug/render.py new file mode 100644 index 00000000..2580241d --- /dev/null +++ b/marker/debug/render.py @@ -0,0 +1,62 @@ +import requests +from PIL import ImageDraw, ImageFont, Image + +from marker.settings import settings +import os + + +def get_font_path() -> str: + font_path = settings.DEBUG_RENDER_FONT + + if not os.path.exists(font_path): + os.makedirs(os.path.dirname(font_path), exist_ok=True) + font_dl_path = f"{settings.RECOGNITION_FONT_DL_BASE}/{os.path.basename(font_path)}" + with requests.get(font_dl_path, stream=True) as r, open(font_path, 'wb') as f: + r.raise_for_status() + for chunk in r.iter_content(chunk_size=8192): + f.write(chunk) + + return font_path + + +def get_text_size(text, font): + im = Image.new(mode="P", size=(0, 0)) + draw = ImageDraw.Draw(im) + _, _, width, height = draw.textbbox((0, 0), text=text, font=font) + return width, height + + +def render_on_image(bboxes, image, labels=None, label_offset=1, label_font_size=10, color: str | list='red', draw_bbox=True): + draw = ImageDraw.Draw(image) + font_path = get_font_path() + label_font = ImageFont.truetype(font_path, label_font_size) + + for i, bbox in enumerate(bboxes): + bbox = [int(p) for p in bbox] + if draw_bbox: + draw.rectangle(bbox, outline=color[i] if isinstance(color, list) else color, width=1) + + if labels is not None: + label = labels[i] + text_position = ( + bbox[0] + label_offset, + bbox[1] + label_offset + ) + text_size = get_text_size(label, label_font) + if text_size[0] <= 0 or text_size[1] <= 0: + continue + box_position = ( + text_position[0], + text_position[1], + text_position[0] + text_size[0], + text_position[1] + text_size[1] + ) + draw.rectangle(box_position, fill="white") + draw.text( + text_position, + label, + fill=color[i] if isinstance(color, list) else color, + font=label_font + ) + + return image \ No newline at end of file diff --git a/marker/equations/equations.py b/marker/equations/equations.py index 6c796e21..1407265a 100644 --- a/marker/equations/equations.py +++ b/marker/equations/equations.py @@ -2,12 +2,11 @@ from copy import deepcopy from typing import List -from marker.debug.data import dump_equation_debug_data from marker.equations.inference import get_total_texify_tokens, get_latex_batched from marker.pdf.images import render_bbox_image from marker.schema.bbox import rescale_bbox from marker.schema.page import Page -from marker.schema.block import Line, Span, Block, bbox_from_lines, split_block_lines, find_insert_block +from marker.schema.block import Line, Span, Block, split_block_lines, find_insert_block from marker.settings import settings @@ -177,7 +176,4 @@ def replace_equations(doc, pages: List[Page], texify_model, batch_multiplier=1): successful_ocr += success_count unsuccessful_ocr += fail_count - # If debug mode is on, dump out conversions for comparison - dump_equation_debug_data(doc, images, converted_spans) - return pages, {"successful_ocr": successful_ocr, "unsuccessful_ocr": unsuccessful_ocr, "equations": eq_count} diff --git a/marker/layout/layout.py b/marker/layout/layout.py index 1d94f3c0..0e2e92cd 100644 --- a/marker/layout/layout.py +++ b/marker/layout/layout.py @@ -1,9 +1,11 @@ +from collections import defaultdict from typing import List from surya.layout import batch_layout_detection from marker.pdf.images import render_image from marker.schema.bbox import rescale_bbox +from marker.schema.block import bbox_from_lines from marker.schema.page import Page from marker.settings import settings @@ -41,8 +43,56 @@ def annotate_block_types(pages: List[Page]): for i, block in enumerate(page.blocks): block = page.blocks[i] - block_type = "Text" - if i in max_intersections: + block_type = None + if i in max_intersections and max_intersections[i][0] > 0.0: j = max_intersections[i][1] block_type = page.layout.bboxes[j].label - block.block_type = block_type \ No newline at end of file + block.block_type = block_type + + # Smarter block layout assignment - first assign same as closest block + # Next, fall back to text + for i, block in enumerate(page.blocks): + if block.block_type is not None: + continue + min_dist = 0 + min_dist_idx = None + for j, block2 in enumerate(page.blocks): + if j == i or block2.block_type is None: + continue + dist = block.distance(block2.bbox) + if min_dist_idx is None or dist < min_dist: + min_dist = dist + min_dist_idx = j + if min_dist_idx is not None: + block.block_type = page.blocks[min_dist_idx].block_type + + for i, block in enumerate(page.blocks): + if block.block_type is None: + block.block_type = "text" + + # Merge blocks together, preserving pdf order + curr_layout_idx = None + curr_layout_block = None + new_blocks = [] + for i in range(len(page.blocks)): + if i not in max_intersections: + if curr_layout_block is not None: + curr_layout_block.bbox = bbox_from_lines(curr_layout_block.lines) + new_blocks.append(curr_layout_block) + curr_layout_block = None + curr_layout_idx = None + new_blocks.append(page.blocks[i]) + elif max_intersections[i][1] != curr_layout_idx: + if curr_layout_block is not None: + curr_layout_block.bbox = bbox_from_lines(curr_layout_block.lines) + new_blocks.append(curr_layout_block) + curr_layout_block = page.blocks[i].copy() + curr_layout_idx = max_intersections[i][1] + else: + curr_layout_block.lines.extend(page.blocks[i].lines) + + if curr_layout_block is not None: + curr_layout_block.bbox = bbox_from_lines(curr_layout_block.lines) + new_blocks.append(curr_layout_block) + + page.blocks = new_blocks diff --git a/marker/layout/order.py b/marker/layout/order.py index 7cf04edb..64ad98ed 100644 --- a/marker/layout/order.py +++ b/marker/layout/order.py @@ -66,4 +66,10 @@ def sort_blocks_in_reading_order(pages: List[Page]): block_group = sort_block_group(block_groups[position]) new_blocks.extend(block_group) + # Ensure we properly put footers at the end of the page + footer_blocks = [b for b in new_blocks if b.block_type in ["Footnote", "Page-footer"]] + header_blocks = [b for b in new_blocks if b.block_type in ["Page-header"]] + regular_blocks = [b for b in new_blocks if b.block_type not in ["Footnote", "Page-footer", "Page-header"]] + + new_blocks = header_blocks + regular_blocks + footer_blocks page.blocks = new_blocks \ No newline at end of file diff --git a/marker/models.py b/marker/models.py index 4764d84e..877d95e1 100644 --- a/marker/models.py +++ b/marker/models.py @@ -2,7 +2,6 @@ os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" # For some reason, transformers decided to use .isin for a simple op, which is not supported on MPS -from marker.postprocessors.editor import load_editing_model from surya.model.detection.model import load_model as load_detection_model, load_processor as load_detection_processor from texify.model.model import load_model as load_texify_model from texify.model.processor import load_processor as load_texify_processor @@ -11,6 +10,17 @@ from surya.model.recognition.processor import load_processor as load_recognition_processor from surya.model.ordering.model import load_model as load_order_model from surya.model.ordering.processor import load_processor as load_order_processor +from surya.model.table_rec.model import load_model as load_table_model +from surya.model.table_rec.processor import load_processor as load_table_processor + + +def setup_table_rec_model(device=None, dtype=None): + if device: + table_model = load_table_model(device=device, dtype=dtype) + else: + table_model = load_table_model() + table_model.processor = load_table_processor() + return table_model def setup_recognition_model(device=None, dtype=None): @@ -18,8 +28,7 @@ def setup_recognition_model(device=None, dtype=None): rec_model = load_recognition_model(device=device, dtype=dtype) else: rec_model = load_recognition_model() - rec_processor = load_recognition_processor() - rec_model.processor = rec_processor + rec_model.processor = load_recognition_processor() return rec_model @@ -28,9 +37,7 @@ def setup_detection_model(device=None, dtype=None): model = load_detection_model(device=device, dtype=dtype) else: model = load_detection_model() - - processor = load_detection_processor() - model.processor = processor + model.processor = load_detection_processor() return model @@ -39,8 +46,7 @@ def setup_texify_model(device=None, dtype=None): texify_model = load_texify_model(checkpoint=settings.TEXIFY_MODEL_NAME, device=device, dtype=dtype) else: texify_model = load_texify_model(checkpoint=settings.TEXIFY_MODEL_NAME, device=settings.TORCH_DEVICE_MODEL, dtype=settings.TEXIFY_DTYPE) - texify_processor = load_texify_processor() - texify_model.processor = texify_processor + texify_model.processor = load_texify_processor() return texify_model @@ -49,8 +55,7 @@ def setup_layout_model(device=None, dtype=None): model = load_detection_model(checkpoint=settings.LAYOUT_MODEL_CHECKPOINT, device=device, dtype=dtype) else: model = load_detection_model(checkpoint=settings.LAYOUT_MODEL_CHECKPOINT) - processor = load_detection_processor(checkpoint=settings.LAYOUT_MODEL_CHECKPOINT) - model.processor = processor + model.processor = load_detection_processor(checkpoint=settings.LAYOUT_MODEL_CHECKPOINT) return model @@ -59,12 +64,11 @@ def setup_order_model(device=None, dtype=None): model = load_order_model(device=device, dtype=dtype) else: model = load_order_model() - processor = load_order_processor() - model.processor = processor + model.processor = load_order_processor() return model -def load_all_models(device=None, dtype=None, force_load_ocr=False): +def load_all_models(device=None, dtype=None): if device is not None: assert dtype is not None, "Must provide dtype if device is provided" @@ -72,10 +76,10 @@ def load_all_models(device=None, dtype=None, force_load_ocr=False): detection = setup_detection_model(device, dtype) layout = setup_layout_model(device, dtype) order = setup_order_model(device, dtype) - edit = load_editing_model(device, dtype) # Only load recognition model if we'll need it for all pdfs ocr = setup_recognition_model(device, dtype) texify = setup_texify_model(device, dtype) - model_lst = [texify, layout, order, edit, detection, ocr] + table_model = setup_table_rec_model(device, dtype) + model_lst = [texify, layout, order, detection, ocr, table_model] return model_lst \ No newline at end of file diff --git a/marker/ocr/recognition.py b/marker/ocr/recognition.py index 44c22b12..0ebfe060 100644 --- a/marker/ocr/recognition.py +++ b/marker/ocr/recognition.py @@ -1,4 +1,5 @@ import tempfile +from copy import deepcopy from itertools import repeat from typing import List, Optional, Dict @@ -12,6 +13,7 @@ from marker.ocr.heuristics import should_ocr_page, no_text_found, detect_bad_ocr from marker.ocr.lang import langs_to_ids from marker.pdf.images import render_image +from marker.schema.bbox import rescale_bbox from marker.schema.page import Page from marker.schema.block import Block, Line, Span from marker.settings import settings @@ -65,30 +67,40 @@ def run_ocr(doc, pages: List[Page], langs: List[str], rec_model, batch_multiplie def surya_recognition(doc, page_idxs, langs: List[str], rec_model, pages: List[Page], batch_multiplier=1) -> List[Optional[Page]]: + # Slice images in higher resolution than detection happened in images = [render_image(doc[pnum], dpi=settings.SURYA_OCR_DPI) for pnum in page_idxs] + box_scale = settings.SURYA_OCR_DPI / settings.SURYA_DETECTOR_DPI + processor = rec_model.processor selected_pages = [p for i, p in enumerate(pages) if i in page_idxs] surya_langs = [langs] * len(page_idxs) detection_results = [p.text_lines.bboxes for p in selected_pages] - polygons = [[b.polygon for b in bboxes] for bboxes in detection_results] + polygons = deepcopy([[b.polygon for b in bboxes] for bboxes in detection_results]) + + # Scale polygons to get correct image slices + for poly in polygons: + for p in poly: + for i in range(len(p)): + p[i] = [int(p[i][0] * box_scale), int(p[i][1] * box_scale)] results = run_recognition(images, surya_langs, rec_model, processor, polygons=polygons, batch_size=int(get_batch_size() * batch_multiplier)) new_pages = [] - for (page_idx, result, old_page) in zip(page_idxs, results, selected_pages): + for idx, (page_idx, result, old_page) in enumerate(zip(page_idxs, results, selected_pages)): text_lines = old_page.text_lines ocr_results = result.text_lines blocks = [] for i, line in enumerate(ocr_results): + scaled_bbox = rescale_bbox([0, 0, images[idx].size[0], images[idx].size[1]], old_page.text_lines.image_bbox, line.bbox) block = Block( - bbox=line.bbox, + bbox=scaled_bbox, pnum=page_idx, lines=[Line( - bbox=line.bbox, + bbox=scaled_bbox, spans=[Span( text=line.text, - bbox=line.bbox, + bbox=scaled_bbox, span_id=f"{page_idx}_{i}", font="", font_weight=0, @@ -101,7 +113,7 @@ def surya_recognition(doc, page_idxs, langs: List[str], rec_model, pages: List[P page = Page( blocks=blocks, pnum=page_idx, - bbox=result.image_bbox, + bbox=old_page.text_lines.image_bbox, rotation=0, text_lines=text_lines, ocr_method="surya" diff --git a/marker/output.py b/marker/output.py index 4216216e..269975ff 100644 --- a/marker/output.py +++ b/marker/output.py @@ -30,7 +30,7 @@ def save_markdown(out_folder, fname, full_text, images, out_metadata): with open(markdown_filepath, "w+", encoding='utf-8') as f: f.write(full_text) with open(out_meta_filepath, "w+") as f: - f.write(json.dumps(out_metadata, indent=4)) + f.write(json.dumps(out_metadata, indent=4, ensure_ascii=False)) for filename, image in images.items(): image_filepath = os.path.join(subfolder_path, filename) diff --git a/marker/pdf/extract_text.py b/marker/pdf/extract_text.py index 937ded31..07719e95 100644 --- a/marker/pdf/extract_text.py +++ b/marker/pdf/extract_text.py @@ -2,8 +2,8 @@ from typing import List, Optional, Dict import pypdfium2 as pdfium -import pypdfium2.internal as pdfium_i +from marker.cleaners.toc import get_pdf_toc from marker.pdf.utils import font_flags_decomposer from marker.settings import settings from marker.schema.block import Span, Line, Block @@ -17,8 +17,8 @@ def pdftext_format_to_blocks(page, pnum: int) -> Page: page_blocks = [] span_id = 0 for block_idx, block in enumerate(page["blocks"]): - block_lines = [] for l in block["lines"]: + block_lines = [] spans = [] for i, s in enumerate(l["spans"]): block_text = s["text"] @@ -44,14 +44,16 @@ def pdftext_format_to_blocks(page, pnum: int) -> Page: # Only select valid lines, with positive bboxes if line_obj.area >= 0: block_lines.append(line_obj) - block_obj = Block( - lines=block_lines, - bbox=block["bbox"], - pnum=pnum - ) - # Only select blocks with lines - if len(block_lines) > 0: - page_blocks.append(block_obj) + + # Each block is a single line + block_obj = Block( + lines=block_lines, + bbox=l["bbox"], + pnum=pnum + ) + # Only select blocks with lines + if len(block_lines) > 0: + page_blocks.append(block_obj) page_bbox = page["bbox"] page_width = abs(page_bbox[2] - page_bbox[0]) @@ -75,7 +77,7 @@ def pdftext_format_to_blocks(page, pnum: int) -> Page: def get_text_blocks(doc, fname, max_pages: Optional[int] = None, start_page: Optional[int] = None) -> (List[Page], Dict): - toc = get_toc(doc) + toc = get_pdf_toc(doc) if start_page: assert start_page < len(doc) @@ -90,7 +92,7 @@ def get_text_blocks(doc, fname, max_pages: Optional[int] = None, start_page: Opt page_range = range(start_page, start_page + max_pages) - char_blocks = dictionary_output(fname, page_range=page_range, keep_chars=True, workers=settings.PDFTEXT_CPU_WORKERS) + char_blocks = dictionary_output(fname, page_range=page_range, keep_chars=False, workers=settings.PDFTEXT_CPU_WORKERS) marker_blocks = [pdftext_format_to_blocks(page, pnum) for pnum, page in enumerate(char_blocks)] return marker_blocks, toc @@ -105,23 +107,6 @@ def naive_get_text(doc): return full_text -def get_toc(doc, max_depth=15): - toc = doc.get_toc(max_depth=max_depth) - toc_list = [] - for item in toc: - list_item = { - "title": item.title, - "level": item.level, - "is_closed": item.is_closed, - "n_kids": item.n_kids, - "page_index": item.page_index, - "view_mode": pdfium_i.ViewmodeToStr.get(item.view_mode), - "view_pos": item.view_pos, - } - toc_list.append(list_item) - return toc_list - - def get_length_of_text(fname: str) -> int: doc = pdfium.PdfDocument(fname) text = naive_get_text(doc).strip() diff --git a/marker/postprocessors/editor.py b/marker/postprocessors/editor.py deleted file mode 100644 index 48695fbb..00000000 --- a/marker/postprocessors/editor.py +++ /dev/null @@ -1,123 +0,0 @@ -from collections import defaultdict -from itertools import chain -from typing import Optional - -from marker.settings import settings -import torch -import torch.nn.functional as F -from marker.postprocessors.t5 import T5ForTokenClassification, byt5_tokenize - - -def get_batch_size(): - if settings.EDITOR_BATCH_SIZE is not None: - return settings.EDITOR_BATCH_SIZE - elif settings.TORCH_DEVICE_MODEL == "cuda": - return 12 - return 6 - - -def load_editing_model(device=None, dtype=None): - if not settings.ENABLE_EDITOR_MODEL: - return None - - if device: - model = T5ForTokenClassification.from_pretrained( - settings.EDITOR_MODEL_NAME, - torch_dtype=dtype, - device=device, - ) - else: - model = T5ForTokenClassification.from_pretrained( - settings.EDITOR_MODEL_NAME, - torch_dtype=settings.MODEL_DTYPE, - ).to(settings.TORCH_DEVICE_MODEL) - model.eval() - - model.config.label2id = { - "equal": 0, - "delete": 1, - "newline-1": 2, - "space-1": 3, - } - model.config.id2label = {v: k for k, v in model.config.label2id.items()} - return model - - -def edit_full_text(text: str, model: Optional[T5ForTokenClassification], batch_multiplier=1) -> (str, dict): - if not model: - return text, {} - - batch_size = get_batch_size() * batch_multiplier - tokenized = byt5_tokenize(text, settings.EDITOR_MAX_LENGTH) - input_ids = tokenized["input_ids"] - char_token_lengths = tokenized["char_token_lengths"] - - # Run model - token_masks = [] - for i in range(0, len(input_ids), batch_size): - batch_input_ids = tokenized["input_ids"][i: i + batch_size] - batch_input_ids = torch.tensor(batch_input_ids, device=model.device) - batch_attention_mask = tokenized["attention_mask"][i: i + batch_size] - batch_attention_mask = torch.tensor(batch_attention_mask, device=model.device) - with torch.inference_mode(): - predictions = model(batch_input_ids, attention_mask=batch_attention_mask) - - logits = predictions.logits.cpu() - - # If the max probability is less than a threshold, we assume it's a bad prediction - # We want to be conservative to not edit the text too much - probs = F.softmax(logits, dim=-1) - max_prob = torch.max(probs, dim=-1) - cutoff_prob = max_prob.values < settings.EDITOR_CUTOFF_THRESH - labels = logits.argmax(-1) - labels[cutoff_prob] = model.config.label2id["equal"] - labels = labels.squeeze().tolist() - if len(labels) == settings.EDITOR_MAX_LENGTH: - labels = [labels] - labels = list(chain.from_iterable(labels)) - token_masks.extend(labels) - - # List of characters in the text - flat_input_ids = list(chain.from_iterable(input_ids)) - - # Strip special tokens 0,1. Keep unknown token, although it should never be used - assert len(token_masks) == len(flat_input_ids) - token_masks = [mask for mask, token in zip(token_masks, flat_input_ids) if token >= 2] - - assert len(token_masks) == len(list(text.encode("utf-8"))) - - edit_stats = defaultdict(int) - out_text = [] - start = 0 - for i, char in enumerate(text): - char_token_length = char_token_lengths[i] - masks = token_masks[start: start + char_token_length] - labels = [model.config.id2label[mask] for mask in masks] - if all(l == "delete" for l in labels): - # If we delete whitespace, roll with it, otherwise ignore - if char.strip(): - out_text.append(char) - else: - edit_stats["delete"] += 1 - elif labels[0] == "newline-1": - out_text.append("\n") - out_text.append(char) - edit_stats["newline-1"] += 1 - elif labels[0] == "space-1": - out_text.append(" ") - out_text.append(char) - edit_stats["space-1"] += 1 - else: - out_text.append(char) - edit_stats["equal"] += 1 - - start += char_token_length - - out_text = "".join(out_text) - return out_text, edit_stats - - - - - - diff --git a/marker/postprocessors/markdown.py b/marker/postprocessors/markdown.py index 005f2b4b..33f77146 100644 --- a/marker/postprocessors/markdown.py +++ b/marker/postprocessors/markdown.py @@ -66,24 +66,26 @@ def merge_spans(pages: List[Page]) -> List[List[MergedBlock]]: lines=block_lines, pnum=block.pnum, bbox=block.bbox, - block_type=block.block_type + block_type=block.block_type, + heading_level=block.heading_level )) merged_blocks.append(page_blocks) return merged_blocks -def block_surround(text, block_type): +def block_surround(text, block_type, heading_level): if block_type == "Section-header": if not text.startswith("#"): - text = "\n## " + text.strip().title() + "\n" + asterisks = "#" * heading_level if heading_level is not None else "##" + text = f"\n{asterisks} " + text.strip().title() + "\n" elif block_type == "Title": if not text.startswith("#"): text = "# " + text.strip().title() + "\n" elif block_type == "Table": text = "\n" + text + "\n" elif block_type == "List-item": - text = escape_markdown(text) + text = escape_markdown(text.rstrip()) + "\n" elif block_type == "Code": text = "\n```\n" + text + "\n```\n" elif block_type == "Text": @@ -92,6 +94,8 @@ def block_surround(text, block_type): if text.strip().startswith("$$") and text.strip().endswith("$$"): text = text.strip() text = "\n" + text + "\n" + elif block_type == "Caption": + text = "\n" + escape_markdown(text) + "\n" return text @@ -130,56 +134,75 @@ def line_separator(line1, line2, block_type, is_continuation=False): return line1 + "\n" + line2 -def block_separator(line1, line2, block_type1, block_type2): +def block_separator(prev_block: FullyMergedBlock, block: FullyMergedBlock): sep = "\n" - if block_type1 == "Text": + if prev_block.block_type == "Text": sep = "\n\n" - return sep + line2 + if prev_block.page_end: + sep = settings.PAGE_SEPARATOR + + return sep + block.text -def merge_lines(blocks: List[List[MergedBlock]]): +def merge_lines(blocks: List[List[MergedBlock]], max_block_gap=15): text_blocks = [] prev_type = None prev_line = None block_text = "" block_type = "" + prev_heading_level = None for idx, page in enumerate(blocks): for block in page: block_type = block.block_type - if block_type != prev_type and prev_type: + if (block_type != prev_type and prev_type) or (block.heading_level != prev_heading_level and prev_heading_level): text_blocks.append( FullyMergedBlock( - text=block_surround(block_text, prev_type), - block_type=prev_type + text=block_surround(block_text, prev_type, prev_heading_level), + block_type=prev_type, + page_end=False ) ) block_text = "" prev_type = block_type + prev_heading_level = block.heading_level # Join lines in the block together properly for i, line in enumerate(block.lines): line_height = line.bbox[3] - line.bbox[1] prev_line_height = prev_line.bbox[3] - prev_line.bbox[1] if prev_line else 0 prev_line_x = prev_line.bbox[0] if prev_line else 0 + vertical_dist = min(abs(line.bbox[1] - prev_line.bbox[3]), abs(line.bbox[3] - prev_line.bbox[1])) if prev_line else 0 prev_line = line - is_continuation = line_height == prev_line_height and line.bbox[0] == prev_line_x + is_continuation = line_height == prev_line_height and line.bbox[0] == prev_line_x and vertical_dist < max_block_gap if block_text: block_text = line_separator(block_text, line.text, block_type, is_continuation) else: block_text = line.text - if settings.PAGINATE_OUTPUT and idx < len(blocks) - 1: - block_text += "\n\n" + "-" * 16 + "\n\n" # Page separator horizontal rule + # Force blocks to end at page boundaries + if settings.PAGINATE_OUTPUT: + text_blocks.append( + FullyMergedBlock( + text=block_surround(block_text, prev_type, prev_heading_level), + block_type=prev_type, + page_end=True + ) + ) + block_text = "" + # Append the final block text_blocks.append( FullyMergedBlock( - text=block_surround(block_text, prev_type), - block_type=block_type + text=block_surround(block_text, prev_type, prev_heading_level), + block_type=block_type, + page_end=False ) ) + + text_blocks = [block for block in text_blocks if block.text.strip()] return text_blocks @@ -188,7 +211,7 @@ def get_full_text(text_blocks): prev_block = None for block in text_blocks: if prev_block: - full_text += block_separator(prev_block.text, block.text, prev_block.block_type, block.block_type) + full_text += block_separator(prev_block, block) else: full_text += block.text prev_block = block diff --git a/marker/postprocessors/t5.py b/marker/postprocessors/t5.py deleted file mode 100644 index dac471a8..00000000 --- a/marker/postprocessors/t5.py +++ /dev/null @@ -1,141 +0,0 @@ -from transformers import T5Config, T5PreTrainedModel -import torch -from torch import nn -from copy import deepcopy -from typing import Optional, Tuple, Union -from itertools import chain - -from transformers.modeling_outputs import TokenClassifierOutput -from transformers.models.t5.modeling_t5 import T5Stack -from transformers.utils.model_parallel_utils import get_device_map, assert_device_map - - -def byt5_tokenize(text: str, max_length: int, pad_token_id: int = 0): - byte_codes = [] - for char in text: - # Add 3 to account for special tokens - byte_codes.append([byte + 3 for byte in char.encode('utf-8')]) - - tokens = list(chain.from_iterable(byte_codes)) - # Map each token to the character it represents - char_token_lengths = [len(b) for b in byte_codes] - - batched_tokens = [] - attention_mask = [] - for i in range(0, len(tokens), max_length): - batched_tokens.append(tokens[i:i + max_length]) - attention_mask.append([1] * len(batched_tokens[-1])) - - # Pad last item - if len(batched_tokens[-1]) < max_length: - batched_tokens[-1] += [pad_token_id] * (max_length - len(batched_tokens[-1])) - attention_mask[-1] += [0] * (max_length - len(attention_mask[-1])) - - return {"input_ids": batched_tokens, "attention_mask": attention_mask, "char_token_lengths": char_token_lengths} - - - - -# From https://github.com/osainz59/t5-encoder -class T5ForTokenClassification(T5PreTrainedModel): - _keys_to_ignore_on_load_missing = [r"encoder.embed_tokens.weight"] - - def __init__(self, config: T5Config): - super().__init__(config) - self.model_dim = config.d_model - - self.shared = nn.Embedding(config.vocab_size, config.d_model) - - encoder_config = deepcopy(config) - encoder_config.is_decoder = False - encoder_config.is_encoder_decoder = False - encoder_config.use_cache = False - self.encoder = T5Stack(encoder_config, self.shared) - - classifier_dropout = ( - config.classifier_dropout if hasattr(config, 'classifier_dropout') else config.dropout_rate - ) - self.dropout = nn.Dropout(classifier_dropout) - self.classifier = nn.Linear(config.d_model, config.num_labels) - - # Initialize weights and apply final processing - self.post_init() - - # Model parallel - self.model_parallel = False - self.device_map = None - - - def parallelize(self, device_map=None): - self.device_map = ( - get_device_map(len(self.encoder.block), range(torch.cuda.device_count())) - if device_map is None - else device_map - ) - assert_device_map(self.device_map, len(self.encoder.block)) - self.encoder.parallelize(self.device_map) - self.classifier.to(self.encoder.first_device) - self.model_parallel = True - - def deparallelize(self): - self.encoder.deparallelize() - self.encoder = self.encoder.to("cpu") - self.classifier = self.classifier.to("cpu") - self.model_parallel = False - self.device_map = None - torch.cuda.empty_cache() - - def get_input_embeddings(self): - return self.shared - - def set_input_embeddings(self, new_embeddings): - self.shared = new_embeddings - self.encoder.set_input_embeddings(new_embeddings) - - def get_encoder(self): - return self.encoder - - def _prune_heads(self, heads_to_prune): - for layer, heads in heads_to_prune.items(): - self.encoder.block[layer].layer[0].SelfAttention.prune_heads(heads) - - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple[torch.FloatTensor], TokenClassifierOutput]: - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = self.encoder( - input_ids=input_ids, - attention_mask=attention_mask, - inputs_embeds=inputs_embeds, - head_mask=head_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - sequence_output = outputs[0] - - sequence_output = self.dropout(sequence_output) - logits = self.classifier(sequence_output) - - loss = None - - if not return_dict: - output = (logits,) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return TokenClassifierOutput( - loss=loss, - logits=logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions - ) \ No newline at end of file diff --git a/marker/schema/bbox.py b/marker/schema/bbox.py index 0469374a..e035dcbf 100644 --- a/marker/schema/bbox.py +++ b/marker/schema/bbox.py @@ -58,6 +58,10 @@ def unnormalize_box(bbox, width, height): ] +def get_center(bbox): + return [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2] + + class BboxElement(BaseModel): bbox: List[float] @@ -93,6 +97,11 @@ def intersection_pct(self, other_bbox: List[float]): return 0.0 return box_intersection_pct(self.bbox, other_bbox) + def distance(self, other_bbox: List[float]): + bbox_center = get_center(self.bbox) + other_center = get_center(other_bbox) + return ((bbox_center[0] - other_center[0]) ** 2 + (bbox_center[1] - other_center[1]) ** 2) ** 0.5 + def rescale_bbox(orig_dim, new_dim, bbox): page_width, page_height = new_dim[2] - new_dim[0], new_dim[3] - new_dim[1] diff --git a/marker/schema/block.py b/marker/schema/block.py index 1ecd1a6a..03d4d864 100644 --- a/marker/schema/block.py +++ b/marker/schema/block.py @@ -45,6 +45,7 @@ class Block(BboxElement): lines: List[Line] pnum: int block_type: Optional[str] = None + heading_level: Optional[int] = None @property def prelim_text(self): diff --git a/marker/schema/merged.py b/marker/schema/merged.py index e53ea412..6462da64 100644 --- a/marker/schema/merged.py +++ b/marker/schema/merged.py @@ -19,8 +19,10 @@ class MergedBlock(BboxElement): lines: List[MergedLine] pnum: int block_type: Optional[str] + heading_level: Optional[int] = None class FullyMergedBlock(BaseModel): text: str block_type: str + page_end: bool diff --git a/marker/settings.py b/marker/settings.py index 6d6bf7e7..beaccf62 100644 --- a/marker/settings.py +++ b/marker/settings.py @@ -4,6 +4,7 @@ from pydantic import computed_field from pydantic_settings import BaseSettings import torch +import os class Settings(BaseSettings): @@ -12,6 +13,7 @@ class Settings(BaseSettings): IMAGE_DPI: int = 96 # DPI to render images pulled from pdf at EXTRACT_IMAGES: bool = True # Extract images from pdfs and save them PAGINATE_OUTPUT: bool = False # Paginate output markdown + BASE_DIR: str = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) @computed_field @property @@ -47,7 +49,7 @@ def TORCH_DEVICE_MODEL(self) -> str: OCR_ALL_PAGES: bool = False # Run OCR on every page even if text can be extracted ## Surya - SURYA_OCR_DPI: int = 96 + SURYA_OCR_DPI: int = 192 RECOGNITION_BATCH_SIZE: Optional[int] = None # Batch size for surya OCR defaults to 64 for cuda, 32 otherwise ## Tesseract @@ -64,7 +66,7 @@ def TORCH_DEVICE_MODEL(self) -> str: # Layout model SURYA_LAYOUT_DPI: int = 96 - BAD_SPAN_TYPES: List[str] = ["Page-footer", "Page-header", "Picture"] # You can add "Caption" and "Footnote" here to get rid of those elements + BAD_SPAN_TYPES: List[str] = ["Page-footer", "Page-header", "Picture"] # You can add "Caption" and "Footnote" here to get rid of those elements - this just removes the text, not the image in case of Picture LAYOUT_MODEL_CHECKPOINT: str = "vikp/surya_layout3" BBOX_INTERSECTION_THRESH: float = 0.7 # How much the layout and pdf bboxes need to overlap to be the same TABLE_INTERSECTION_THRESH: float = 0.7 @@ -75,17 +77,23 @@ def TORCH_DEVICE_MODEL(self) -> str: ORDER_BATCH_SIZE: Optional[int] = None # Defaults to 12 for cuda, 6 otherwise ORDER_MAX_BBOXES: int = 255 - # Final editing model - EDITOR_BATCH_SIZE: Optional[int] = None # Defaults to 6 for cuda, 12 otherwise - EDITOR_MAX_LENGTH: int = 1024 - EDITOR_MODEL_NAME: str = "vikp/pdf_postprocessor_t5" - ENABLE_EDITOR_MODEL: bool = False # The editor model can create false positives - EDITOR_CUTOFF_THRESH: float = 0.9 # Ignore predictions below this probability + # Table models + SURYA_TABLE_DPI: int = 192 + + # Headings + HEADING_LEVEL_COUNT: int = 4 + HEADING_MERGE_THRESHOLD: float = .25 + HEADING_DEFAULT_LEVEL: int = 2 + + # Output + PAGE_SEPARATOR: str = "\n\n" + "-" * 48 + "\n\n" # Debug - DEBUG: bool = False # Enable debug logging - DEBUG_DATA_FOLDER: Optional[str] = None - DEBUG_LEVEL: int = 0 # 0 to 2, 2 means log everything + DEBUG_DATA_FOLDER: str = os.path.join(BASE_DIR, "debug_data") + DEBUG: bool = False + FONT_DIR: str = os.path.join(BASE_DIR, "static", "fonts") + DEBUG_RENDER_FONT: str = os.path.join(FONT_DIR, "GoNotoCurrent-Regular.ttf") + FONT_DL_BASE: str = "https://github.com/satbyy/go-noto-universal/releases/download/v7.0" @computed_field @property diff --git a/marker/tables/cells.py b/marker/tables/cells.py deleted file mode 100644 index 46d83484..00000000 --- a/marker/tables/cells.py +++ /dev/null @@ -1,112 +0,0 @@ -from marker.schema.bbox import rescale_bbox, box_intersection_pct -from marker.schema.page import Page -import numpy as np -from sklearn.cluster import DBSCAN -from marker.settings import settings - - -def cluster_coords(coords, row_count): - if len(coords) == 0: - return [] - coords = np.array(sorted(set(coords))).reshape(-1, 1) - - clustering = DBSCAN(eps=.01, min_samples=max(2, row_count // 4)).fit(coords) - clusters = clustering.labels_ - - separators = [] - for label in set(clusters): - clustered_points = coords[clusters == label] - separators.append(np.mean(clustered_points)) - - separators = sorted(separators) - return separators - - -def find_column_separators(page: Page, table_box, rows, round_factor=.002, min_count=1): - left_edges = [] - right_edges = [] - centers = [] - - line_boxes = [p.bbox for p in page.text_lines.bboxes] - line_boxes = [rescale_bbox(page.text_lines.image_bbox, page.bbox, l) for l in line_boxes] - line_boxes = [l for l in line_boxes if box_intersection_pct(l, table_box) > settings.BBOX_INTERSECTION_THRESH] - - pwidth = page.bbox[2] - page.bbox[0] - pheight = page.bbox[3] - page.bbox[1] - for cell in line_boxes: - ncell = [cell[0] / pwidth, cell[1] / pheight, cell[2] / pwidth, cell[3] / pheight] - left_edges.append(ncell[0] / round_factor * round_factor) - right_edges.append(ncell[2] / round_factor * round_factor) - centers.append((ncell[0] + ncell[2]) / 2 * round_factor / round_factor) - - left_edges = [l for l in left_edges if left_edges.count(l) > min_count] - right_edges = [r for r in right_edges if right_edges.count(r) > min_count] - centers = [c for c in centers if centers.count(c) > min_count] - - sorted_left = cluster_coords(left_edges, len(rows)) - sorted_right = cluster_coords(right_edges, len(rows)) - sorted_center = cluster_coords(centers, len(rows)) - - # Find list with minimum length - separators = max([sorted_left, sorted_right, sorted_center], key=len) - separators.append(1) - separators.insert(0, 0) - return separators - - -def assign_cells_to_columns(page, table_box, rows, round_factor=.002, tolerance=.01): - separators = find_column_separators(page, table_box, rows, round_factor=round_factor) - additional_column_index = 0 - pwidth = page.bbox[2] - page.bbox[0] - row_dicts = [] - - for row in rows: - new_row = {} - last_col_index = -1 - for cell in row: - left_edge = cell[0][0] / pwidth - column_index = -1 - for i, separator in enumerate(separators): - if left_edge - tolerance < separator and last_col_index < i: - column_index = i - break - if column_index == -1: - column_index = len(separators) + additional_column_index - additional_column_index += 1 - new_row[column_index] = cell[1] - last_col_index = column_index - additional_column_index = 0 - row_dicts.append(new_row) - - max_row_idx = 0 - for row in row_dicts: - max_row_idx = max(max_row_idx, max(row.keys())) - - # Assign sorted cells to columns, account for blanks - new_rows = [] - for row in row_dicts: - flat_row = [] - for row_idx in range(1, max_row_idx + 1): - if row_idx in row: - flat_row.append(row[row_idx]) - else: - flat_row.append("") - new_rows.append(flat_row) - - # Pad rows to have the same length - max_row_len = max([len(r) for r in new_rows]) - for row in new_rows: - while len(row) < max_row_len: - row.append("") - - cols_to_remove = set() - for idx, col in enumerate(zip(*new_rows)): - col_total = sum([len(cell.strip()) > 0 for cell in col]) - if col_total == 0: - cols_to_remove.add(idx) - - rows = [] - for row in new_rows: - rows.append([col for idx, col in enumerate(row) if idx not in cols_to_remove]) - - return rows \ No newline at end of file diff --git a/marker/tables/edges.py b/marker/tables/edges.py deleted file mode 100644 index 9bf30ba1..00000000 --- a/marker/tables/edges.py +++ /dev/null @@ -1,122 +0,0 @@ -import math - -import cv2 -import numpy as np - - -def get_detected_lines_sobel(image): - sobelx = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3) - - scaled_sobel = np.uint8(255 * sobelx / np.max(sobelx)) - - kernel = np.ones((4, 1), np.uint8) - eroded = cv2.erode(scaled_sobel, kernel, iterations=1) - scaled_sobel = cv2.dilate(eroded, kernel, iterations=3) - - return scaled_sobel - - -def get_line_angle(x1, y1, x2, y2): - slope = (y2 - y1) / (x2 - x1) - - angle_radians = math.atan(slope) - angle_degrees = math.degrees(angle_radians) - - return angle_degrees - - -def get_detected_lines(image, slope_tol_deg=10): - new_image = image.astype(np.float32) * 255 # Convert to 0-255 range - new_image = get_detected_lines_sobel(new_image) - new_image = new_image.astype(np.uint8) - - edges = cv2.Canny(new_image, 50, 200, apertureSize=3) - - lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=50, minLineLength=2, maxLineGap=100) - - line_info = [] - if lines is not None: - for line in lines: - x1, y1, x2, y2 = line[0] - bbox = [x1, y1, x2, y2] - - vertical = False - if x2 == x1: - vertical = True - else: - line_angle = get_line_angle(x1, y1, x2, y2) - if 90 - slope_tol_deg < line_angle < 90 + slope_tol_deg: - vertical = True - elif -90 - slope_tol_deg < line_angle < -90 + slope_tol_deg: - vertical = True - if not vertical: - continue - - if bbox[3] < bbox[1]: - bbox[1], bbox[3] = bbox[3], bbox[1] - if bbox[2] < bbox[0]: - bbox[0], bbox[2] = bbox[2], bbox[0] - if vertical: - line_info.append(bbox) - return line_info - - -def get_vertical_lines(image, divisor=2, x_tolerance=10, y_tolerance=1): - vertical_lines = get_detected_lines(image) - - vertical_lines = sorted(vertical_lines, key=lambda x: x[0]) - for line in vertical_lines: - for i in range(0, len(line)): - line[i] = (line[i] // divisor) * divisor - - # Merge adjacent line segments together - to_remove = [] - for i, line in enumerate(vertical_lines): - for j, line2 in enumerate(vertical_lines): - if j <= i: - continue - if line[0] != line2[0]: - continue - - expanded_line1 = [line[0], line[1] - y_tolerance, line[2], - line[3] + y_tolerance] - - line1_points = set(range(int(expanded_line1[1]), int(expanded_line1[3]))) - line2_points = set(range(int(line2[1]), int(line2[3]))) - intersect_y = len(line1_points.intersection(line2_points)) > 0 - - if intersect_y: - vertical_lines[j][1] = min(line[1], line2[1]) - vertical_lines[j][3] = max(line[3], line2[3]) - to_remove.append(i) - - vertical_lines = [line for i, line in enumerate(vertical_lines) if i not in to_remove] - - # Remove redundant segments - to_remove = [] - for i, line in enumerate(vertical_lines): - if i in to_remove: - continue - for j, line2 in enumerate(vertical_lines): - if j <= i or j in to_remove: - continue - close_in_x = abs(line[0] - line2[0]) < x_tolerance - line1_points = set(range(int(line[1]), int(line[3]))) - line2_points = set(range(int(line2[1]), int(line2[3]))) - - intersect_y = len(line1_points.intersection(line2_points)) > 0 - - if close_in_x and intersect_y: - # Keep the longer line and extend it - if len(line2_points) > len(line1_points): - vertical_lines[j][1] = min(line[1], line2[1]) - vertical_lines[j][3] = max(line[3], line2[3]) - to_remove.append(i) - else: - vertical_lines[i][1] = min(line[1], line2[1]) - vertical_lines[i][3] = max(line[3], line2[3]) - to_remove.append(j) - - vertical_lines = [line for i, line in enumerate(vertical_lines) if i not in to_remove] - - return vertical_lines \ No newline at end of file diff --git a/marker/tables/table.py b/marker/tables/table.py index 280ae2a5..1cb16a12 100644 --- a/marker/tables/table.py +++ b/marker/tables/table.py @@ -1,155 +1,113 @@ -from marker.schema.bbox import merge_boxes, box_intersection_pct, rescale_bbox +from tqdm import tqdm +from pypdfium2 import PdfDocument +from tabled.assignment import assign_rows_columns +from tabled.formats import formatter +from tabled.inference.detection import merge_tables + +from surya.input.pdflines import get_page_text_lines +from tabled.inference.recognition import get_cells, recognize_tables + +from marker.pdf.images import render_image +from marker.schema.bbox import rescale_bbox from marker.schema.block import Line, Span, Block from marker.schema.page import Page -from tabulate import tabulate from typing import List from marker.settings import settings -from marker.tables.cells import assign_cells_to_columns -from marker.tables.utils import sort_table_blocks, replace_dots, replace_newlines - - -def get_table_surya(page, table_box, space_tol=.01) -> List[List[str]]: - table_rows = [] - table_row = [] - x_position = None - sorted_blocks = sort_table_blocks(page.blocks) - for block_idx, block in enumerate(sorted_blocks): - sorted_lines = sort_table_blocks(block.lines) - for line_idx, line in enumerate(sorted_lines): - line_bbox = line.bbox - intersect_pct = box_intersection_pct(line_bbox, table_box) - if intersect_pct < settings.TABLE_INTERSECTION_THRESH or len(line.spans) == 0: - continue - normed_x_start = line_bbox[0] / page.width - normed_x_end = line_bbox[2] / page.width - cells = [[s.bbox, s.text] for s in line.spans] - if x_position is None or normed_x_start > x_position - space_tol: - # Same row - table_row.extend(cells) - else: - # New row - if len(table_row) > 0: - table_rows.append(table_row) - table_row = cells - x_position = normed_x_end - if len(table_row) > 0: - table_rows.append(table_row) - table_rows = assign_cells_to_columns(page, table_box, table_rows) - return table_rows - - -def get_table_pdftext(page: Page, table_box, space_tol=.01, round_factor=4) -> List[List[str]]: - page_width = page.width - table_rows = [] - table_cell = "" - cell_bbox = None - table_row = [] - sorted_char_blocks = sort_table_blocks(page.char_blocks) - - table_width = table_box[2] - table_box[0] - new_line_start_x = table_box[0] + table_width * .3 - table_width_pct = (table_width / page_width) * .95 - - for block_idx, block in enumerate(sorted_char_blocks): - sorted_lines = sort_table_blocks(block["lines"]) - for line_idx, line in enumerate(sorted_lines): - line_bbox = line["bbox"] - intersect_pct = box_intersection_pct(line_bbox, table_box) - if intersect_pct < settings.TABLE_INTERSECTION_THRESH: - continue - for span in line["spans"]: - for char in span["chars"]: - x_start, y_start, x_end, y_end = char["bbox"] - x_start /= page_width - x_end /= page_width - fullwidth_cell = False - - if cell_bbox is not None: - # Find boundaries of cell bbox before merging - cell_x_start, cell_y_start, cell_x_end, cell_y_end = cell_bbox - cell_x_start /= page_width - cell_x_end /= page_width - - fullwidth_cell = cell_x_end - cell_x_start >= table_width_pct - - cell_content = replace_dots(replace_newlines(table_cell)) - if cell_bbox is None: # First char - table_cell += char["char"] - cell_bbox = char["bbox"] - # Check if we are in the same cell, ensure cell is not full table width (like if stray text gets included in the table) - elif (cell_x_start - space_tol < x_start < cell_x_end + space_tol) and not fullwidth_cell: - table_cell += char["char"] - cell_bbox = merge_boxes(cell_bbox, char["bbox"]) - # New line and cell - # Use x_start < new_line_start_x to account for out-of-order cells in the pdf - elif x_start < cell_x_end - space_tol and x_start < new_line_start_x: - if len(table_cell) > 0: - table_row.append((cell_bbox, cell_content)) - table_cell = char["char"] - cell_bbox = char["bbox"] - if len(table_row) > 0: - table_row = sorted(table_row, key=lambda x: round(x[0][0] / round_factor)) - table_rows.append(table_row) - table_row = [] - else: # Same line, new cell, check against cell bbox - if len(table_cell) > 0: - table_row.append((cell_bbox, cell_content)) - table_cell = char["char"] - cell_bbox = char["bbox"] - - if len(table_cell) > 0: - table_row.append((cell_bbox, replace_dots(replace_newlines(table_cell)))) - if len(table_row) > 0: - table_row = sorted(table_row, key=lambda x: round(x[0][0] / round_factor)) - table_rows.append(table_row) - - total_cells = sum([len(row) for row in table_rows]) - if total_cells > 0: - table_rows = assign_cells_to_columns(page, table_box, table_rows) - return table_rows - else: - return [] - - -def merge_tables(page_table_boxes): - # Merge tables that are next to each other - expansion_factor = 1.02 - shrink_factor = .98 - ignore_boxes = set() - for i in range(len(page_table_boxes)): - if i in ignore_boxes: + +def get_table_boxes(pages: List[Page], doc: PdfDocument, fname): + table_imgs = [] + table_counts = [] + table_bboxes = [] + img_sizes = [] + + for page in pages: + pnum = page.pnum + # The bbox for the entire table + bbox = [b.bbox for b in page.layout.bboxes if b.label == "Table"] + + if len(bbox) == 0: + table_counts.append(0) + img_sizes.append(None) continue - for j in range(i + 1, len(page_table_boxes)): - if j in ignore_boxes: - continue - expanded_box1 = [page_table_boxes[i][0] * shrink_factor, page_table_boxes[i][1], - page_table_boxes[i][2] * expansion_factor, page_table_boxes[i][3]] - expanded_box2 = [page_table_boxes[j][0] * shrink_factor, page_table_boxes[j][1], - page_table_boxes[j][2] * expansion_factor, page_table_boxes[j][3]] - if box_intersection_pct(expanded_box1, expanded_box2) > 0: - page_table_boxes[i] = merge_boxes(page_table_boxes[i], page_table_boxes[j]) - ignore_boxes.add(j) - return [b for i, b in enumerate(page_table_boxes) if i not in ignore_boxes] + highres_img = render_image(doc[pnum], dpi=settings.SURYA_TABLE_DPI) + + page_table_imgs = [] + page_bboxes = [] + + # Merge tables that are next to each other + bbox = merge_tables(bbox) + + # Number of tables per page + table_counts.append(len(bbox)) + img_sizes.append(highres_img.size) + + for bb in bbox: + highres_bb = rescale_bbox(page.layout.image_bbox, [0, 0, highres_img.size[0], highres_img.size[1]], bb) + page_table_imgs.append(highres_img.crop(highres_bb)) + page_bboxes.append(highres_bb) + + table_imgs.extend(page_table_imgs) + table_bboxes.extend(page_bboxes) + + table_idxs = [i for i, c in enumerate(table_counts) if c > 0] + sel_text_lines = get_page_text_lines( + fname, + table_idxs, + [hr for i, hr in enumerate(img_sizes) if i in table_idxs], + ) + text_lines = [] + out_img_sizes = [] + for i in range(len(table_counts)): + if i in table_idxs: + page_ocred = pages[i].ocr_method is not None + if page_ocred: + # This will force re-detection of cells if the page was ocred (the text lines are not accurate) + text_lines.extend([None] * table_counts[i]) + else: + text_lines.extend([sel_text_lines.pop(0)] * table_counts[i]) + out_img_sizes.extend([img_sizes[i]] * table_counts[i]) + + assert len(table_imgs) == len(table_bboxes) == len(text_lines) == len(out_img_sizes) + assert sum(table_counts) == len(table_imgs) + return table_imgs, table_bboxes, table_counts, text_lines, out_img_sizes + + +def format_tables(pages: List[Page], doc: PdfDocument, fname: str, detection_model, table_rec_model, ocr_model): + det_models = [detection_model, detection_model.processor] + rec_models = [table_rec_model, table_rec_model.processor, ocr_model, ocr_model.processor] + + # Don't look at table cell detection tqdm output + tqdm.disable = True + table_imgs, table_boxes, table_counts, table_text_lines, img_sizes = get_table_boxes(pages, doc, fname) + cells, needs_ocr = get_cells(table_imgs, table_boxes, img_sizes, table_text_lines, det_models, detect_boxes=settings.OCR_ALL_PAGES) + tqdm.disable = False + + # This will redo OCR if OCR is forced, since we need to redetect bounding boxes, etc. + table_rec = recognize_tables(table_imgs, cells, needs_ocr, rec_models) + cells = [assign_rows_columns(tr, im_size) for tr, im_size in zip(table_rec, img_sizes)] + table_md = [formatter("markdown", cell)[0] for cell in cells] -def format_tables(pages: List[Page]): - # Formats tables nicely into github flavored markdown table_count = 0 - for page in pages: + for page_idx, page in enumerate(pages): + page_table_count = table_counts[page_idx] + if page_table_count == 0: + continue + table_insert_points = {} blocks_to_remove = set() pnum = page.pnum - - page_table_boxes = [b for b in page.layout.bboxes if b.label == "Table"] - page_table_boxes = [rescale_bbox(page.layout.image_bbox, page.bbox, b.bbox) for b in page_table_boxes] - page_table_boxes = merge_tables(page_table_boxes) + highres_size = img_sizes[table_count] + page_table_boxes = table_boxes[table_count:table_count + page_table_count] for table_idx, table_box in enumerate(page_table_boxes): + lowres_table_box = rescale_bbox([0, 0, highres_size[0], highres_size[1]], page.bbox, table_box) + for block_idx, block in enumerate(page.blocks): - intersect_pct = block.intersection_pct(table_box) + intersect_pct = block.intersection_pct(lowres_table_box) if intersect_pct > settings.TABLE_INTERSECTION_THRESH and block.block_type == "Table": if table_idx not in table_insert_points: table_insert_points[table_idx] = max(0, block_idx - len(blocks_to_remove)) # Where to insert the new table @@ -163,17 +121,10 @@ def format_tables(pages: List[Page]): for table_idx, table_box in enumerate(page_table_boxes): if table_idx not in table_insert_points: + table_count += 1 continue - if page.ocr_method == "surya": - table_rows = get_table_surya(page, table_box) - else: - table_rows = get_table_pdftext(page, table_box) - # Skip empty tables - if len(table_rows) == 0: - continue - - 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