Diard is a document image analysis pipeline to extract semi-structured Analysis Ready Data from your Document's Images. To achieve this, the state-of-the-art layout detection model is used (DiT) to extract document objects (e.g., title, text, list, figure,...) along with table-transformer for table extraction (note: TE was not tested thoroughly and should be updated for serious use). These objects are then used to segment the sections (e.g., Table Of Contents, Introduction,...) and to find the information needed to obtain a semi-structured version of your document. The pipeline output can be exported as HTML for evaluation and as JSON for text analysis.
First, clone the repository and use the setup guide to run things locally.
git clone https://github.com/peetio/Diard.git
cd DiardIf you just want to test the pipeline, go ahead and use the following command to run the example script.
The script has an argument which allows you to skip previously processed documents. If you don't want to skip documents, remove the '--overwrite' argument from the command below. Secondly, you can skip documents which the pipeline is not able to process instead of exiting the program by adding the '--skip-failures' argument like we did below.
python main.py --overwrite --skip-failuresAfter runnnig the above command, you should see output similar to the one below in your terminal.
Processing 'example': 0%| | 0/8 [00:00<?, ?it/s]
2022-05-16 09:46:34,138 | INFO: Language detection successful! Language is now set to German (deu).
Processing 'example': 12%|█████████████████▎ | 1/8 [00:03<00:25, 3.61s/it]For more detailed explanations on how the pipeline can be used, you can refer to the examples. Please note that the main Python script should always be ran from the root of the repository.
Diard
│
│ main.py # The document image analysis pipeline
│ requirements.txt # List of required Python libraries
│ README.md # This file
│ LICENCSE # Apache 2.0 License
│
+---ditod # Microsoft's DiT modules
+---detr # Microsoft's DETR modules
|
+---modules
│ │
│ │ document.py # Document class definition
│ │ exceptions.py # Custom exceptions
│ │ layoutdetection.py # Layout detection classes & functions
│ │ sections.py # Section segmentation related functions
│ │ export.py # Export/ evaluation related methods (HTML, JSON)
│ │ tables.py # Table extraction classes & functions
│
+---docs
│ │
│ │ setup_guide.md # Environment setup guide
│ │ examples.md # Example code with detailed explanations
│
+---resources
│ │
│ │ stylesheet.css # Stylesheet for HTML visualization
│ │ stylescript.js # Style script for HTML visualization
│ │ structure_config.json # Default args for table extraction
│ │
│ +---images # Images used in docs & README
│ │
│ +---model_configs # Configuration files for DiT
│ │
│ +---pdfs # To be processed pdfs
│ │ │ example.pdf
│ │ │ ...
│ │
│ +---doc_images # Example document images
│ │ │ 1.jpg
│ │ │ 2.jpg
│ │ │ ...
│ │
│ +---weights # Storage for pre-trained model weights
│ │ publaynet_dit-l_cascade.pth # Weights used in initial release (layout detection)
│ │ pubtables1m_structure_de... # Weights used in initial release (table extraction)
│
+---output # Default output dir (created by pipeline)
│
+---example # Directory for each PDF you process
│
+---html # Storage for HTML visualizations
│
+---jsons # Storage for doc layout JSON files
│
+---visualizations # Storage for detection visualizations
- table extraction only works for consistent tables (no varying number of rows / columns per row / column)
- OCR is unable to extract single digits,- could be fixed by setting a different Page Segmentation Method (PSM)
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