Enterprise / Document AI
Scanned PDF Data Extraction System
A web-based document intelligence platform that automatically extracts structured data from scanned PDFs — identifying document types, company names, addresses, and dates — turning unstructured paper into searchable, actionable data.
90%+
Extraction accuracy
50x
Faster than manual
Cloud
Scalable processing
The Challenge
The client had thousands of scanned contract PDFs locked in unstructured formats. Legal and operations teams spent hours manually reading documents to extract key fields — company names, effective dates, clause types, and addresses. They needed an automated system that could process documents at scale and feed structured data into their existing workflows.
Our Approach
We built a three-stage pipeline: first, image preprocessing and OCR to convert scanned pages into raw text. Then, NLP models classify the document type and extract named entities (organizations, dates, addresses). Finally, a web interface lets users upload documents, review extracted data, and export structured results. The entire system runs on cloud-native infrastructure for elastic scaling.
The Results
Automated extraction of 12+ entity types from contracts
OCR accuracy improved through custom image preprocessing
Document classification by type (contract, invoice, letter)
Web UI for upload, review, and bulk export
Reduced document processing time from hours to seconds
Scalable cloud architecture for burst workloads
System Architecture
PDF Upload
→
Image Preprocessing
→
OCR Engine
→
NLP / NER
→
Structured Output
Python
NLP / NER
Document AI
Computer Vision
OCR
FastAPI
Cloud-Native