Intelligent Document Processing
AIA modern web application for extracting structured data from PDF documents using OpenAI's GPT models. Designed for business documents such as invoices, forms, and reports, the app supports both text-based and image-based (multimodal) extraction strategies. The system provides accurate data extraction with high reliability for various document types.
The Problem
Businesses needed to extract structured data from various PDF documents (invoices, forms, reports) but manual data entry was time-consuming and error-prone. Existing OCR solutions struggled with complex layouts and contextual understanding.
The Process
Developed a multimodal approach using OpenAI's GPT models for both text and image-based extraction. Implemented preprocessing pipelines for document optimization and created validation systems to ensure data accuracy.
My Role & Contribution
Lead AI engineer responsible for designing the extraction pipeline, implementing LangChain workflows, developing the web interface, and optimizing model performance for various document types.
Challenges & Solutions
Handling diverse document layouts and formats while balancing accuracy with processing speed. Solved by implementing adaptive extraction strategies and creating document-type-specific processing pipelines.
Outcome & Impact
Achieved 95% accuracy in data extraction across multiple document types. Reduced manual data entry time by 85% and processing costs by 60% compared to traditional OCR solutions.
Key Features
- • Text and image-based document extraction
- • Support for multiple document formats
- • Structured data output in JSON/CSV formats
- • Batch processing capabilities
- • Quality validation and error handling