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Adding a sample for identifying PII in a PDF (#1023)
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| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Annotating PII in a PDF\n", | ||
| "\n", | ||
| "This sample takes a PDF as an input, extracts the text, identifies PII using Presidio and annotates the PII using highlight annotations.\n", | ||
| "\n", | ||
| "## Prerequisites\n", | ||
| "Before getting started, make sure the following packages are installed. For detailed documentation, see the [installation docs](https://microsoft.github.io/presidio/installation).\n", | ||
| "\n", | ||
| "Install from PyPI:\n", | ||
| "```bash\n", | ||
| "pip install presidio_analyzer\n", | ||
| "pip install presidio_anonymizer\n", | ||
| "python -m spacy download en_core_web_lg\n", | ||
| "pip install pdfminer.six\n", | ||
| "pip install pikepdf\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# For Presidio\n", | ||
| "from presidio_analyzer import AnalyzerEngine, PatternRecognizer\n", | ||
| "from presidio_anonymizer import AnonymizerEngine\n", | ||
| "from presidio_anonymizer.entities import OperatorConfig\n", | ||
| "\n", | ||
| "# For console output\n", | ||
| "from pprint import pprint\n", | ||
| "\n", | ||
| "# For extracting text\n", | ||
| "from pdfminer.high_level import extract_text, extract_pages\n", | ||
| "from pdfminer.layout import LTTextContainer, LTChar, LTTextLine\n", | ||
| "\n", | ||
| "# For updating the PDF\n", | ||
| "from pikepdf import Pdf, AttachedFileSpec, Name, Dictionary, Array" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Analyze the text in the PDF\n", | ||
| "\n", | ||
| "To extract the text from the PDF, we use the pdf miner library. We extract the text from the PDF at the text container level. This is roughly equivalent to a paragraph. \n", | ||
| "\n", | ||
| "We then use Presidio Analyzer to identify the PII and it's location in the text.\n", | ||
| "\n", | ||
| "The Presidio analyzer is using pre-defined entity recognizers, and offers the option to create custom recognizers.\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "analyzer = AnalyzerEngine()\n", | ||
| "\n", | ||
| "analyzed_character_sets = []\n", | ||
| "\n", | ||
| "for page_layout in extract_pages(\"./sample_data/sample.pdf\"):\n", | ||
| " for text_container in page_layout:\n", | ||
| " if isinstance(text_container, LTTextContainer):\n", | ||
| "\n", | ||
| " # The element is a LTTextContainer, containing a paragraph of text.\n", | ||
| " text_to_anonymize = text_container.get_text()\n", | ||
| "\n", | ||
| " # Analyze the text using the analyzer engine\n", | ||
| " analyzer_results = analyzer.analyze(text=text_to_anonymize, language='en')\n", | ||
| " \n", | ||
| " if text_to_anonymize.isspace() == False:\n", | ||
| " print(text_to_anonymize)\n", | ||
| " print(analyzer_results)\n", | ||
| "\n", | ||
| " characters = list([])\n", | ||
| "\n", | ||
| " # Grab the characters from the PDF\n", | ||
| " for text_line in filter(lambda t: isinstance(t, LTTextLine), text_container):\n", | ||
| " for character in filter(lambda t: isinstance(t, LTChar), text_line):\n", | ||
| " characters.append(character)\n", | ||
| "\n", | ||
| "\n", | ||
| " # Slice out the characters that match the analyzer results.\n", | ||
| " for result in analyzer_results:\n", | ||
| " start = result.start\n", | ||
| " end = result.end\n", | ||
| " analyzed_character_sets.append({\"characters\": characters[start:end], \"result\": result})" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Create phrase bounding boxes\n", | ||
| "\n", | ||
| "The next task is to take the character data, and inflate it into full phrase bounding boxes.\n", | ||
| "\n", | ||
| "For example, for an email address, we'll turn the bounding boxes for each character in the email address into one single bounding box." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Combine the bounding boxes into a single bounding box.\n", | ||
| "def combine_rect(rectA, rectB):\n", | ||
| " a, b = rectA, rectB\n", | ||
| " startX = min( a[0], b[0] )\n", | ||
| " startY = min( a[1], b[1] )\n", | ||
| " endX = max( a[2], b[2] )\n", | ||
| " endY = max( a[3], b[3] )\n", | ||
| " return (startX, startY, endX, endY)\n", | ||
| "\n", | ||
| "analyzed_bounding_boxes = []\n", | ||
| "\n", | ||
| "# For each character set, combine the bounding boxes into a single bounding box.\n", | ||
| "for analyzed_character_set in analyzed_character_sets:\n", | ||
| " completeBoundingBox = analyzed_character_set[\"characters\"][0].bbox\n", | ||
| " \n", | ||
| " for character in analyzed_character_set[\"characters\"]:\n", | ||
| " completeBoundingBox = combine_rect(completeBoundingBox, character.bbox)\n", | ||
| " \n", | ||
| " analyzed_bounding_boxes.append({\"boundingBox\": completeBoundingBox, \"result\": analyzed_character_set[\"result\"]})" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Add highlight annotations\n", | ||
| "\n", | ||
| "We finally iterate through all the analyzed bounding boxes and create highlight annotations for all of them." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "pdf = Pdf.open(\"./sample_data/sample.pdf\")\n", | ||
| "\n", | ||
| "annotations = []\n", | ||
| "\n", | ||
| "# Create a highlight annotation for each bounding box.\n", | ||
| "for analyzed_bounding_box in analyzed_bounding_boxes:\n", | ||
| "\n", | ||
| " boundingBox = analyzed_bounding_box[\"boundingBox\"]\n", | ||
| "\n", | ||
| " # Create the annotation. \n", | ||
| " # We could also create a redaction annotation if the ongoing workflows supports them.\n", | ||
| " highlight = Dictionary(\n", | ||
| " Type=Name.Annot,\n", | ||
| " Subtype=Name.Highlight,\n", | ||
| " QuadPoints=[boundingBox[0], boundingBox[3],\n", | ||
| " boundingBox[2], boundingBox[3],\n", | ||
| " boundingBox[0], boundingBox[1],\n", | ||
| " boundingBox[2], boundingBox[1]],\n", | ||
| " Rect=[boundingBox[0], boundingBox[1], boundingBox[2], boundingBox[3]],\n", | ||
| " C=[1, 0, 0],\n", | ||
| " CA=0.5,\n", | ||
| " T=analyzed_bounding_box[\"result\"].entity_type,\n", | ||
| " )\n", | ||
| " \n", | ||
| " annotations.append(highlight)\n", | ||
| "\n", | ||
| "# Add the annotations to the PDF.\n", | ||
| "pdf.pages[0].Annots = pdf.make_indirect(annotations)\n", | ||
| "\n", | ||
| "# And save.\n", | ||
| "pdf.save(\"./sample_data/sample_annotated.pdf\")" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Result\n", | ||
| "\n", | ||
| "The output from the samples above creates a new PDF. This contains the original content, with text highlight annotations where the PII has been found.\n", | ||
| "\n", | ||
| "Each text annotation contains the name of the entity found.\n", | ||
| "\n", | ||
| "## Note\n", | ||
| "\n", | ||
| "Before relying on this methodology to detect or markup PII from a PDF, please be aware of the following:\n", | ||
| "\n", | ||
| "### Text extraction\n", | ||
| "\n", | ||
| "We purposely use a different library specifically for extracting text from the PDF. This is because text extraction is hard to get right, and it's worth using a library specifically developed with the purpose in mind.\n", | ||
| "\n", | ||
| "For more details, see:\n", | ||
| "\n", | ||
| "[https://pdfminersix.readthedocs.io/en/latest/topic/converting_pdf_to_text.html](https://pdfminersix.readthedocs.io/en/latest/topic/converting_pdf_to_text.html)\n", | ||
| "\n", | ||
| "That said, even with a purpose built library, there may be occasions where PII is present and visible in a PDF, but it is not detected by the sample code.\n", | ||
| "\n", | ||
| "This includes, but is not limited to:\n", | ||
| "\n", | ||
| "- Text that cannot be reliable extracted to be analyzed. (e.g. incorrect spacing, wrong reading order)\n", | ||
| "- Text present in previous iterations of the PDF which is hidden from text extraction. (See incremental editing)\n", | ||
| "- Text present in images. (requires OCRing)\n", | ||
| "- Text present in annotations." | ||
| ] | ||
| } | ||
| ], | ||
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| "display_name": ".venv", | ||
| "language": "python", | ||
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| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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