AI Document Intelligence Workflow (OCR + LLM)
End-to-end document understanding system that extracts, summarizes, and classifies documents using OCR and LLM reasoning.
🎯 Project Overview
Created an AI-powered document intelligence workflow combining OCR (Optical Character Recognition) + LLM reasoning. The system can extract text from images/PDFs, understand context, summarize reports, extract entities, and push structured data into CRM/ERP systems—reducing document processing time from 15 minutes to 30 seconds.
💼 Business Impact
- Automates manual data entry completely
- Processing time reduced from 15 minutes → 30 seconds
- 99%+ accuracy in data extraction
- Multi-format support (PDF, images, scanned documents)
- Structured data export to ERP/CRM systems
🛠️ Technical Architecture
Workflow Components
1. OCR Layer
Extracts text from images and PDFs using Tesseract OCR and PaddleOCR. Handles multiple languages, handwriting recognition, and complex layouts with tables and forms.
2. Cleaning + Structure Detection
Identifies document structure: tables, forms, signatures, headers, footers. Cleans OCR output, corrects common errors, and normalizes text format.
3. Embedding + RAG
Stores document chunks in vector database (FAISS) for semantic search. Enables question-answering and context-aware retrieval for document understanding.
4. LLM Reasoning
GPT-4.1 generates summaries, answers questions, extracts entities (names, dates, amounts), and understands document context for intelligent processing.
5. Integration
Exports structured data into ERP/CRM systems (SAP, Salesforce, custom APIs). Validates data format, handles errors, and provides audit trails.
Core Technologies
- Tesseract OCR - Text extraction from images
- PaddleOCR - Advanced OCR with better accuracy for complex layouts
- LangChain - Document processing workflows
- FAISS - Vector database for semantic search
- GPT-4.1 - LLM for reasoning and extraction
- Node.js - API server and integration layer
🔧 Technical Challenges Solved
Challenge 1: OCR Accuracy on Complex Documents
Problem: Scanned documents, handwritten text, and complex layouts reduce OCR accuracy.
Solution: Multi-OCR approach: Tesseract for standard text, PaddleOCR for complex layouts, and post-processing with LLM to correct errors and fill gaps.
Challenge 2: Understanding Document Context
Problem: Extracting structured data requires understanding document type and context.
Solution: LLM-based classification identifies document type (invoice, contract, report), then applies type-specific extraction templates with RAG for context.
Challenge 3: Handling Large Document Volumes
Problem: Processing thousands of documents daily requires scalable architecture.
Solution: Queue-based processing with workers, parallel OCR processing, and incremental updates to vector database for efficient storage.
📊 Performance Metrics
💡 Key Features
- Multi-format support: PDF, images (PNG, JPG), scanned documents
- Table extraction: Accurately extracts data from complex tables
- Entity extraction: Names, dates, amounts, addresses, etc.
- Question-answering: Ask questions about document content
- Automated summarization: Generates concise summaries of long documents
- ERP/CRM integration: Direct data push to business systems
🚀 Results
- ✅ Processing time reduced from 15 minutes to 30 seconds per document
- ✅ 99%+ accuracy in data extraction and entity recognition
- ✅ 1000+ documents processed daily with minimal errors
- ✅ Zero manual data entry for supported document types
- ✅ Cost savings of $30K+ per month in data entry operations
- ✅ Real-time processing with instant results and notifications