new version 2.2.7

.github/workflows/test_labeling.yaml

@@ -31,4 +31,4 @@ jobs:
         python-version: '3.9'
     - name: Run tests for labeling
       run: |
-        test="true" docker-compose -f labeling/docker-compose.yml up --build --exit-code-from test
+        test="true" docker compose -f labeling/docker-compose.yml up --build --exit-code-from test

.github/workflows/test_on_push.yaml

@@ -36,4 +36,4 @@ jobs:
         flake8 .
     - name: Run tests
       run: |
-        test="true" docker-compose up --build --exit-code-from test
+        test="true" docker compose up --build --exit-code-from test

README.md

@@ -17,8 +17,10 @@ It extracts a document’s logical structure and content: tables, text formattin
 The document’s content is represented as a tree storing headings and lists of any level. 
 Dedoc can be integrated in a document contents and structure analysis system as a separate module.
 
-## Workflow
+## Star History
+[![Star History Chart](https://api.star-history.com/svg?repos=ispras/dedoc&type=Date)](https://tar-history.com/#ispras/dedoc&Date)
 
+## Workflow
 ![Workflow](https://github.com/ispras/dedoc/raw/master/docs/source/_static/workflow.png)
 
 Workflow description is given [`here`](https://dedoc.readthedocs.io/en/latest/?badge=latest#workflow)
@@ -136,12 +138,12 @@ cd dedoc
 
 ### 3. Build the image and run the application
 ```shell
-docker-compose up --build
+docker compose up --build
 ```
 
 ### 4. Run container with tests
 ```shell
-test="true" docker-compose up --build
+test="true" docker compose up --build
 ```
 
 If you need to change some application settings, you may update `config.py` according to your needs and re-build the image.

VERSION

@@ -1 +1 @@
-2.2.6
+2.2.7

dedoc/api/api_args.py

@@ -28,6 +28,8 @@ class QueryParameters:
     # pdf handling
     pdf_with_text_layer: str = Form("auto_tabby", enum=["true", "false", "auto", "auto_tabby", "tabby"],
                                     description="Extract text from a text layer of PDF or using OCR methods for image-like documents")
+    fast_textual_layer_detection: str = Form("false", enum=["true", "false"],
+                                             description="Use non-ML solution to detect textual layer. Much faster but less accurate.")
     language: str = Form("rus+eng", description="Recognition language ('rus+eng', 'rus', 'eng', 'fra', 'spa')")
     pages: str = Form(":", description='Page numbers range for reading PDF or images, "left:right" means read pages from left to right')
     is_one_column_document: str = Form("auto", enum=["auto", "true", "false"],

dedoc/api/web/index.html

@@ -128,6 +128,10 @@ <h4>PDF handling</h4>
                     </label>
                 </p>
 
+                <p>
+                    <label><input name="fast_textual_layer_detection" type="checkbox" value="true"> fast_textual_layer_detection</label>
+                </p>
+
                 <p>
                     <label> language
                         <input name="language" list="language" size="8" placeholder="rus+eng">

dedoc/data_structures/concrete_annotations/table_annotation.py

@@ -8,10 +8,10 @@ class TableAnnotation(Annotation):
     """
     name = "table"
 
-    def __init__(self, name: str, start: int, end: int) -> None:
+    def __init__(self, value: str, start: int, end: int) -> None:
         """
-        :param name: unique identifier of the table which is referenced inside this annotation
+        :param value: unique identifier of the table which is referenced inside this annotation
         :param start: start of the annotated text (usually zero)
         :param end: end of the annotated text (usually end of the line)
         """
-        super().__init__(start=start, end=end, name=TableAnnotation.name, value=name, is_mergeable=False)
+        super().__init__(start=start, end=end, name=TableAnnotation.name, value=value, is_mergeable=False)

dedoc/data_structures/line_with_meta.py

@@ -136,6 +136,9 @@ def uid(self) -> str:
     def set_line(self, line: str) -> None:
         self._line = line
 
+    def set_metadata(self, metadata: LineMetadata) -> None:
+        self._metadata = metadata
+
     def __repr__(self) -> str:
         return (f"LineWithMeta({self.line[:65]}, "
                 f"tagHL={self.metadata.tag_hierarchy_level.level_1, self.metadata.tag_hierarchy_level.level_2, self.metadata.tag_hierarchy_level.line_type})")

dedoc/download_models.py

@@ -6,10 +6,10 @@
 """
 model_hash_dict = dict(
     txtlayer_classifier="9ca1de749d8d37147b00a3a228e03ee1776c695f",
-    scan_orientation_efficient_net_b0="9ea283f3d346ae4fdd82463a9f60b5369a3ffb58",
+    scan_orientation_efficient_net_b0="c60812552a1be624476c1e5b58599867b36f8d4e",
     font_classifier="db4481ad60ab050cbb42079b64f97f9e431feb07",
-    paragraph_classifier="c26a10193499d3cbc77ffec9842bece24fa8950b",
-    line_type_classifiers="0568c6e1f49612c0c351f10b80a26dc05f796683",
+    paragraph_classifier="97c4b78bc20d87ec7d53389e09f1ca35c6ade067",
+    line_type_classifiers="6ad0eacbfdea065b658cb6f039d13f75245d51ae",
     fintoc_classifiers="6a907b7d2437c3f61ac9c506f67175207982fae8"
 )
 

dedoc/readers/article_reader/article_reader.py

@@ -235,7 +235,7 @@ def __create_line_with_refs(self, content: List[Tuple[str, Tag]], bib2uid: dict,
                 if subpart.get("type") == "bibr" and target in bib2uid:
                     annotations.append(ReferenceAnnotation(value=bib2uid[target], start=start, end=start + len(sub_text)))
                 if subpart.get("type") == "table" and target in table2uid:
-                    annotations.append(TableAnnotation(name=table2uid[target], start=start, end=start + len(sub_text)))
+                    annotations.append(TableAnnotation(value=table2uid[target], start=start, end=start + len(sub_text)))
                 if subpart.get("type") == "figure" and target in attachment2uid:
                     annotations.append(AttachAnnotation(attach_uid=attachment2uid[target], start=start, end=start + len(sub_text)))
             else:

dedoc/readers/docx_reader/data_structures/docx_document.py

@@ -110,7 +110,7 @@ def __paragraphs2lines(self, image_refs: dict, table_refs: dict, diagram_refs: d
 
             if i in table_refs:
                 for table_uid in table_refs[i]:
-                    annotation = TableAnnotation(name=table_uid, start=0, end=len(line))
+                    annotation = TableAnnotation(value=table_uid, start=0, end=len(line))
                     line.annotations.append(annotation)
 
             paragraph_id += 1

dedoc/readers/docx_reader/data_structures/table.py

@@ -54,11 +54,14 @@ def to_table(self) -> Table:
                 if cell.vMerge:
                     value = cell.vMerge.get("w:val", "continue")
                     if value == "continue":
-                        cell_lines = cell_list[-1][cell_ind].lines
-                        cell_row_list.append(CellWithMeta(lines=cell_lines, colspan=1, rowspan=1, invisible=True))
-                        last_cell_rowspan = cell_list[rowspan_start_info[cell_ind]][cell_ind]
-                        last_cell_rowspan.rowspan += 1
-                        cell_list[rowspan_start_info[cell_ind]][cell_ind] = last_cell_rowspan
+                        if cell_ind in rowspan_start_info:
+                            cell_lines = cell_list[-1][cell_ind].lines
+                            cell_row_list.append(CellWithMeta(lines=cell_lines, colspan=1, rowspan=1, invisible=True))
+                            last_cell_rowspan = cell_list[rowspan_start_info[cell_ind]][cell_ind]
+                            last_cell_rowspan.rowspan += 1
+                            cell_list[rowspan_start_info[cell_ind]][cell_ind] = last_cell_rowspan
+                        else:
+                            cell_row_list.append(CellWithMeta(lines=cell_lines, colspan=grid_span, rowspan=1, invisible=False))
                     elif value == "restart":
                         rowspan_start_info[cell_ind] = row_index
                         cell_row_list.append(CellWithMeta(lines=cell_lines, colspan=grid_span, rowspan=1, invisible=False))

dedoc/readers/html2pdf_reader/html2pdf_reader.py

@@ -47,7 +47,7 @@ def _add_tables(self, document: UnstructuredDocument, tables: Dict[str, Table])
                 line_id += 1
                 lines.append(line)
             elif previous_line is not None:
-                table_annotation = TableAnnotation(name=table_uid, start=0, end=len(line.line))
+                table_annotation = TableAnnotation(value=table_uid, start=0, end=len(line.line))
                 previous_line.annotations.append(table_annotation)
                 tables_result.append(tables[table_uid])
         return UnstructuredDocument(lines=lines, tables=tables_result, attachments=document.attachments)

dedoc/readers/pdf_reader/data_classes/page_with_bboxes.py

@@ -8,7 +8,7 @@
 
 class PageWithBBox:
 
-    def __init__(self, image: ndarray, bboxes: List[TextWithBBox], page_num: int, attachments: List[PdfImageAttachment] = None,
+    def __init__(self, image: Optional[ndarray], bboxes: List[TextWithBBox], page_num: int, attachments: List[PdfImageAttachment] = None,
                  pdf_page_width: Optional[int] = None, pdf_page_height: Optional[int] = None) -> None:
         self.image = image
         self.bboxes = bboxes

dedoc/readers/pdf_reader/pdf_auto_reader/pdf_auto_reader.py

@@ -52,6 +52,7 @@ def read(self, file_path: str, parameters: Optional[dict] = None) -> Unstructure
         Look to the documentation of :meth:`~dedoc.readers.BaseReader.read` to get information about the method's parameters.
         You can also see :ref:`pdf_handling_parameters` to get more information about `parameters` dictionary possible arguments.
         """
+        parameters = {} if parameters is None else parameters
         warnings = []
         txtlayer_parameters = self.txtlayer_detector.detect_txtlayer(path=file_path, parameters=parameters)
 

dedoc/readers/pdf_reader/pdf_auto_reader/txtlayer_detector.py

@@ -29,8 +29,13 @@ def detect_txtlayer(self, path: str, parameters: dict) -> PdfTxtlayerParameters:
         """
         try:
             lines = self.__get_lines_for_predict(path=path, parameters=parameters)
-            is_correct = self.txtlayer_classifier.predict(lines)
-            first_page_correct = self.__is_first_page_correct(lines=lines, is_txt_layer_correct=is_correct)
+            if str(parameters.get("fast_textual_layer_detection", "false")).lower() == "true":
+                is_correct = any(line.line.strip() for line in lines)
+                first_page_lines = [line for line in lines if line.metadata.page_id == 0]
+                first_page_correct = bool(first_page_lines) and any(line.line.strip() for line in first_page_lines)
+            else:
+                is_correct = self.txtlayer_classifier.predict(lines)
+                first_page_correct = self.__is_first_page_correct(lines=lines, is_txt_layer_correct=is_correct)
             return PdfTxtlayerParameters(is_correct_text_layer=is_correct, is_first_page_correct=first_page_correct)
 
         except Exception as e:

dedoc/readers/pdf_reader/pdf_auto_reader/txtlayer_feature_extractor.py

@@ -7,29 +7,19 @@
 
 class TxtlayerFeatureExtractor:
 
-    def __init__(self) -> None:
-        self.eng = "".join(list(map(chr, range(ord("a"), ord("z") + 1))))
-        self.rus = "".join([chr(i) for i in range(ord("а"), ord("а") + 32)] + ["ё"])
-        self.lower_letters = self.eng + self.rus
-        self.upper_letters = self.lower_letters.upper()
-        self.letters = self.upper_letters + self.lower_letters
-        self.digits = "".join([str(i) for i in range(10)])
-        self.special_symbols = "<>~!@#$%^&*_+-/\"|?.,:;'`= "
-        self.brackets = "{}[]()"
-        self.symbols = self.letters + self.digits + self.brackets + self.special_symbols
-
-        self.prohibited_symbols = {s: i for i, s in enumerate("[]<")}
-
     def transform(self, texts: List[str]) -> pd.DataFrame:
+        from dedoc.structure_extractors.feature_extractors.char_features import letters, digits, special_symbols, brackets, rus, eng, prohibited_symbols, \
+            lower_letters, upper_letters, symbols, count_symbols
+
         features = defaultdict(list)
 
         for text in texts:
-            num_letters = self.__count_symbols(text, self.letters)
-            num_digits = self.__count_symbols(text, self.digits)
-            num_special_symbols = self.__count_symbols(text, self.special_symbols)
-            num_brackets = self.__count_symbols(text, self.brackets)
-            num_rus = self.__count_symbols(text, self.rus + self.rus.upper())
-            num_eng = self.__count_symbols(text, self.eng + self.eng.upper())
+            num_letters = count_symbols(text, letters)
+            num_digits = count_symbols(text, digits)
+            num_special_symbols = count_symbols(text, special_symbols)
+            num_brackets = count_symbols(text, brackets)
+            num_rus = count_symbols(text, rus + rus.upper())
+            num_eng = count_symbols(text, eng + eng.upper())
 
             features["letters_proportion"].append(num_letters / len(text))
             features["digits_proportion"].append(num_digits / len(text))
@@ -38,24 +28,24 @@ def transform(self, texts: List[str]) -> pd.DataFrame:
             features["rus_proportion"].append(num_rus / len(text))
             features["eng_proportion"].append(num_eng / len(text))
 
-            for symbol in self.letters + self.digits:
+            for symbol in letters + digits:
                 n = num_letters + num_digits
                 # proportion of occurring english and russian letters
                 features[f"{symbol}_proportion"].append(text.count(symbol) / n if n != 0 else 0.0)
 
-            for symbol in self.special_symbols + self.brackets:
+            for symbol in special_symbols + brackets:
                 # number of symbols
-                symbol_name = symbol if symbol not in self.prohibited_symbols else f"symbol{self.prohibited_symbols[symbol]}"
+                symbol_name = symbol if symbol not in prohibited_symbols else f"symbol{prohibited_symbols[symbol]}"
                 features[f"{symbol_name}_number"].append(text.count(symbol))
 
             # proportion of letters with symbols
             features["all_proportion"].append((num_letters + num_digits + num_brackets + num_special_symbols) / len(text) if len(text) != 0 else 0)
 
-            case_changes = sum(1 for s1, s2 in zip(text[:-1], text[1:]) if (s1 in self.lower_letters) and (s2 in self.upper_letters))
+            case_changes = sum(1 for s1, s2 in zip(text[:-1], text[1:]) if (s1 in lower_letters) and (s2 in upper_letters))
             features["case_changes"].append(case_changes / len(text))
-            symbol_changes = sum(1 for s1, s2 in zip(text[:-1], text[1:]) if (s1 in self.symbols) != (s2 in self.symbols))
+            symbol_changes = sum(1 for s1, s2 in zip(text[:-1], text[1:]) if (s1 in symbols) != (s2 in symbols))
             features["symbol_changes"].append(symbol_changes / len(text))
-            letter_changes = sum(1 for s1, s2 in zip(text[:-1], text[1:]) if (s1 in self.letters) and (s2 not in self.symbols))
+            letter_changes = sum(1 for s1, s2 in zip(text[:-1], text[1:]) if (s1 in letters) and (s2 not in symbols))
             features["letter_changes"].append(letter_changes / len(text))
 
             features["mean_word_length"].append(np.mean([len(word) for word in text.split()]))
@@ -70,6 +60,3 @@ def transform(self, texts: List[str]) -> pd.DataFrame:
             features["median_char_ord"].append(np.median(all_characters_ord))
         features = pd.DataFrame(features)
         return features[sorted(features.columns)].astype(float)
-
-    def __count_symbols(self, text: str, symbol_list: str) -> int:
-        return sum(1 for symbol in text if symbol in symbol_list)

dedoc/readers/pdf_reader/pdf_image_reader/columns_orientation_classifier/columns_orientation_classifier.py

@@ -1,4 +1,5 @@
 import logging
+import os
 import warnings
 from os import path
 from typing import Optional, Tuple
@@ -30,11 +31,9 @@ def __init__(self, on_gpu: bool, checkpoint_path: Optional[str], *, config: dict
     @property
     def net(self) -> ClassificationModelTorch:
         if self._net is None:
+            net = ClassificationModelTorch(self.checkpoint_path)
             if self.checkpoint_path is not None:
-                net = ClassificationModelTorch(path.join(self.checkpoint_path, "scan_orientation_efficient_net_b0.pth"))
                 self._load_weights(net)
-            else:
-                net = ClassificationModelTorch(None)
             self._net = net
         self._net.to(self.device)
         return self._net
@@ -61,17 +60,18 @@ def _set_device(self, on_gpu: bool) -> None:
         self.logger.warning(f"Classifier is set to device {self.device}")
 
     def _load_weights(self, net: ClassificationModelTorch) -> None:
-        path_checkpoint = path.join(self.checkpoint_path, "scan_orientation_efficient_net_b0.pth")
-        if not path.isfile(path_checkpoint):
-            download_from_hub(out_dir=self.checkpoint_path,
+        if not path.isfile(self.checkpoint_path):
+            from dedoc.config import get_config
+            self.checkpoint_path = os.path.join(get_config()["resources_path"], "scan_orientation_efficient_net_b0.pth")
+            download_from_hub(out_dir=os.path.dirname(os.path.abspath(self.checkpoint_path)),
                               out_name="scan_orientation_efficient_net_b0.pth",
                               repo_name="scan_orientation_efficient_net_b0",
                               hub_name="model.pth")
 
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
-            net.load_state_dict(torch.load(path_checkpoint, map_location=self.location))
-            self.logger.info(f"Weights were loaded from {path_checkpoint}")
+            net.load_state_dict(torch.load(self.checkpoint_path, map_location=self.location))
+            self.logger.info(f"Weights were loaded from {self.checkpoint_path}")
 
     def save_weights(self, path_checkpoint: str) -> None:
         torch.save(self.net.state_dict(), path_checkpoint)

dedoc/readers/pdf_reader/pdf_image_reader/line_metadata_extractor/bold_classifier/agglomerative_clusterizer.py

@@ -57,7 +57,7 @@ def __get_f1_homogeneous(self, x: np.ndarray, x_clusters: np.ndarray) -> float:
 
         w1 = np.std(x) * len(x)
         w2 = np.std(x_clust0) * len(x_clust0) + np.std(x_clust1) * len(x_clust1)
-        f1 = w2 / w1
+        f1 = w2 / w1 if w1 != 0. else 0.
         return f1
 
     def __get_f_criterion_homogeneous(self, n: int, p: int = 2) -> float:

dedoc/readers/pdf_reader/pdf_image_reader/line_metadata_extractor/bold_classifier/valley_emphasis_binarizer.py

@@ -7,12 +7,13 @@ def __init__(self, n: int = 5) -> None:
         self.n = n
 
     def binarize(self, image: np.ndarray) -> np.ndarray:
-        gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-        threshold = self.__get_threshold(gray_img)
+        if image.shape[-1] == 3:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        threshold = self.__get_threshold(image)
 
-        gray_img[gray_img <= threshold] = 0
-        gray_img[gray_img > threshold] = 1
-        return gray_img
+        image[image <= threshold] = 0
+        image[image > threshold] = 1
+        return image
 
     def __get_threshold(self, gray_img: np.ndarray) -> int:
         c, x = np.histogram(gray_img, bins=255)
@@ -33,8 +34,8 @@ def __get_threshold(self, gray_img: np.ndarray) -> int:
             omega_1 = omega_1 + c[t] / total
             omega_2 = 1 - omega_1
             mu_k = mu_k + t * (c[t] / total)
-            mu_1 = mu_k / omega_1
-            mu_2 = (sum_val - mu_k) / omega_2
+            mu_1 = mu_k / omega_1 if omega_1 != 0. else 0.
+            mu_2 = (sum_val - mu_k) / omega_2 if omega_2 != 0. else 0.
             sum_of_neighbors = np.sum(c[max(1, t - self.n):min(255, t + self.n)])
             denom = total
             current_var = (1 - sum_of_neighbors / denom) * (omega_1 * mu_1 ** 2 + omega_2 * mu_2 ** 2)

dedoc/readers/pdf_reader/pdf_image_reader/line_metadata_extractor/metadata_extractor.py

@@ -49,7 +49,7 @@ def extract_metadata_and_set_annotations(self, page_with_lines: PageWithBBox, ca
         lines = []
         for bbox in page_with_lines.bboxes:
             lines.append(LineMetadataExtractor.get_line_with_meta(bbox=bbox))
-            if page_with_lines.image.ndim == 3 and page_with_lines.image.shape[2] == 3:
+            if page_with_lines.image is not None and page_with_lines.image.ndim == 3 and page_with_lines.image.shape[2] == 3:
                 color_annotation = self.__get_color_annotation(bbox, page_with_lines.image)
                 bbox.annotations.append(color_annotation)
         self.__add_spacing_annotations(lines)