cv-doc-chunker 0.1.0

A tool for parsing PDF document layouts and chunking content.

CV Document Chunker

A Python package for parsing PDF document layouts using YOLO models, chunking content based on layout, and optionally performing OCR.

Features

• Convert PDF documents to images for processing.
• Detect document layout elements (e.g., paragraphs, tables, figures) using YOLO.
• Process and refine bounding boxes.
• Chunk document content based on detected layout.
• Generate annotated images showing detected elements.
• (Optional) Perform OCR on detected elements using Azure Document Intelligence.
• Save structured document data (layouts, chunks, OCR text) in JSON format.

Installation

Prerequisites

• Python 3.8+
• Pip package manager
• (Optional but Recommended) CUDA-capable GPU for YOLO model inference acceleration.

Steps

1. Clone the Repository (for development or local install):

   git clone <your-repository-url>
   cd cv-doc-parser
   

2. Create and Activate a Virtual Environment:

   python -m venv venv
   source venv/bin/activate  # Linux/macOS
   # venv\Scripts\activate  # Windows
   

3. Install the Package:

   • Editable mode (recommended for development):

     pip install -e .
     

   • Regular install:

     pip install .
     

   • (If published on PyPI):

     # pip install cv-doc-chunker
     

User-Provided Data

This package requires the user to provide certain data externally:

1. Input Directory (input/): Place the PDF documents you want to process in a directory (e.g., input/). You will need to provide the path to your input file(s) when using the package.
2. Models Directory (models/): Download the necessary YOLO model(s) (e.g., doclayout_yolo_docstructbench_imgsz1024.pt) and place them in a dedicated directory (e.g., models/). The path to this directory (or the specific model file) will be needed by the parser.

Usage

(This section needs specific examples based on your library's API)

Provide examples of how to import and use your library functions or the command-line tool.

Example (Conceptual Python Usage):

from cv_doc_chunker import PDFProcessor

# --- User Configuration ---
input_pdf_path = "path/to/your/input/document.pdf" # Path to user's PDF
model_path = "path/to/your/models/doclayout_yolo.pt" # Path to user's model
output_dir = "path/to/your/output/" # Directory to save results

# --- Initialize and Run ---
processor = PDFProcessor(model_path=model_path, output_dir=output_dir)

# Process the document (layout detection, chunking, etc.)
results = processor.process_document(pdf_path=input_pdf_path)

# Optional: Perform OCR (requires Azure setup)
# ocr_results = processor.perform_ocr(results)

print(f"Processing complete. Results saved in {output_dir}")

Example (Conceptual Command-Line Usage):

(Assumes the cv-chunker entry point is configured)

cv-chunker --input path/to/your/input/document.pdf \
           --model path/to/your/models/doclayout_yolo.pt \
           --output path/to/your/output/ \
           [--ocr] [--azure-endpoint YOUR_ENDPOINT] [--azure-key YOUR_KEY]

Note: Update the conceptual examples above with the actual function names, class names, and command-line arguments provided by your cv-doc-chunker package.

Understanding the Output

After running the parser, the following outputs will typically be available in the specified output_dir:

1. {your-document}_parsed.json: JSON file containing the detected document structure (element labels, coordinates, confidence).
2. {your-document}_annotations/: Directory containing annotated images showing the detected elements for each page (if generate_annotations=True).
3. {your-document}_boxes/: Directory containing individual images for each detected element, organized by page number (if save_bounding_boxes=True). This is required for OCR.
4. {your-document}_sorted_text.json: (Only if ocr=True) JSON file containing the extracted text for each element, sorted according to the structure defined in _parsed.json.

If debug mode is enabled (debug_mode=True), additional debug images might be saved, typically in a debug/ subdirectory within the output_dir, showing intermediate steps of the parsing process.

License

This project is licensed under the MIT License - see the LICENSE file for details.