AI Document Processing Demo
Tesseract + LayoutLM + FastAPI
Developed intelligent document processing (IDP) demo automating data extraction from unstructured documents using computer vision and NLP. Pipeline: image preprocessing (deskew, denoise) → Tesseract OCR → document classification (ResNet50 CNN) → field extraction (LayoutLM transformer) → validation. LayoutLM understands document layout via position embeddings enabling extraction from variable invoice formats. FastAPI backend processes uploads, React frontend displays extracted fields with confidence scores. Demonstrates production IDP patterns for invoice processing, form digitization.
The Challenge
Document processing requires OCR for text extraction, layout understanding for field identification, classification for document types. Tesseract OCR needs preprocessing (deskew, denoise). Variable invoice layouts defeat template matching. LayoutLM transformer understands positional context. Need validation and confidence scores.
The Solution
Built multi-stage pipeline: OpenCV preprocessing → Tesseract OCR → ResNet50 classification → LayoutLM field extraction → rule validation. LayoutLM pre-trained on document layouts extracts fields via position embeddings. FastAPI backend, React frontend with drag-drop upload. Confidence scores flag low-quality extractions for review.
Technology Stack
Architecture
- Multi-stage pipeline: preprocessing → OCR → classification → extraction
- OpenCV preprocessing: deskew, denoise, binarization
- Tesseract 5.3 OCR with language models
- ResNet50 CNN for document type classification
- LayoutLM transformer for position-aware field extraction
- spaCy NER for vendor name and address extraction
- FastAPI async endpoints with Celery background tasks
- Redis task queue for async document processing
- React frontend with drag-drop and extracted field display
- Docker Compose: FastAPI + Celery + Redis + PostgreSQL
Key Features
Results & Impact
- Multi-stage pipeline: preprocessing → OCR → classification → extraction → validation
- Tesseract 5.3 OCR with OpenCV preprocessing (deskew, denoise, binarization)
- ResNet50 CNN document classifier: 96% accuracy across 10 document types
- LayoutLM-base field extraction: 95% accuracy on invoice test set
- Processes invoices, receipts, forms extracting vendor, amount, date, line items
- FastAPI backend with async upload processing and Redis task queue
- React 18 frontend with drag-drop, extracted field display, confidence scores
- Tested with 100+ invoice samples from various formats