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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- document-question-answering |
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- ocr |
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- summarization |
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- document-ai |
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pipeline_tag: document-question-answering |
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model_name: docintel |
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--- |
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# 🧾 DOCINTEL — Document AI (Donut-based) |
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**DOCINTEL** extracts structured insights from scanned PDFs and images using **naver-clova-ix/donut-base** (Donut). It supports OCR fallback, entity extraction, and document summarization via Donut on page images. |
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> ⚠️ Install system dependencies: `poppler` and `tesseract` for pdf2image and pytesseract respectively. |
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## Quickstart |
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1. Create venv & install dependencies: |
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```bash |
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python -m venv venv |
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source venv/bin/activate # Windows: venv\Scripts\activate |
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pip install -r requirements.txt |
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``` |
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2. Run API server: |
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```bash |
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uvicorn app:app --host 0.0.0.0 --port 8000 |
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``` |
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3. Upload a PDF and call endpoints (see examples/demo_commands.txt). |
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## Files |
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- `ocr_extractor.py` — PDF→images→OCR pipeline |
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- `pdf_loader.py` — extract embedded text from PDFs |
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- `entity_tagger.py` — regex-based entity extraction |
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- `summarize_doc.py` — DONUT-based summarizer for page images |
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- `app.py` — FastAPI server with upload/summary endpoints |
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## Notes |
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- Donut requires vision-encoder-decoder inference which may need GPU for speed. |
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- For text-only PDFs consider using `extract_text_from_pdf` then a text summarizer instead of Donut. |
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- This repo is a prototype/demo. Validate on your data before production use. |
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