Document Processing & Information Extraction Pipeline

Problem

Project Overview

This project focuses on building an intelligent document processing pipeline that can automatically extract structured information from various document types including PDFs, invoices, contracts, and forms.

Key Features

• Multi-format support: PDFs, images, scanned documents

• AI-powered extraction: Using OCR and NLP for accurate data extraction

• Template recognition: Automatic detection of document types

• Data validation: Built-in checks for accuracy and completeness

The solution should be able to process high-volume document workflows with enterprise-grade reliability and accuracy.

Goals

Success Criteria

• Accuracy: ≥95% data extraction accuracy

• Speed: Process documents in under 10 seconds each

• Scalability: Handle 1000+ documents per hour

• Format Support: PDF, PNG, JPG, TIFF formats

Business Impact: Reduce manual processing time by 80% and eliminate data entry errors

Solutions

• OpenSource Tesseract + Spacy NLP Pipeline (Tesseract OCR, SpaCy NLP, PyPDF2, Pandas, scikit-learn, FastAPI) – AI-Powered Document Pipeline This solution implements a comprehensive document processing workflow using state-of-the-art AI models for optical character recognition (OCR) and natural language…
• Azure Form Recognizer + Document Intelligence Pipeline (Azure Form Recognizer, Azure Document Intelligence, Azure Logic Apps, SharePoint, Power Automate) – Enterprise document processing solution using Azure Form Recognizer for OCR and layout understanding, combined with Azure Document Intelligence for complex document analysis. Includes automated…