Device - Macbook Air

Challenge / Problem Statement

The client faced several industry-specific challenges:
  • Manual data entry from physical documents was error-prone and labor-intensive.
  • High volumes of invoices and claims came in unstructured, inconsistent formats. Timely and accurate extraction was critical for patient care, claims processing, and compliance.
  • Lack of standardized document formats created automation complexity.

Objectives

  • Automate extraction of key data from diverse documents..
  • Standardize outputs for seamless backend integration.
  • Reduce claim processing time and manual overhead.
  • Improve accuracy and consistency in healthcare and insurance workflows.

Process & Implementation

  • Built endpoints in FastAPI with schema validation (Pydantic).
  • Integrated Tesseract OCR & EasyOCR for text extraction.
  • Used spaCy + regex for entity recognition and structured parsing.
  • Applied PDFPlumber & Pillow for preprocessing scanned PDFs and invoices.
  • Designed integration-ready outputs for claim pipelines and dashboards.

Our Solution

We developed a lightweight, scalable Document Extraction API using FastAPI, leveraging OCR and NLP techniques. The solution included four specialized endpoints, each optimized for a specific document type:

Aadhar Card Extractor – Captured name, DOB, address, and Aadhar number via OCR + regex.

PAN Card Extractor – Parsed PAN number, name, and DOB with OCR layers and text-position detection.

Insurance Policy Extractor – Extracted policy number, coverage details, insured name, and premium cycle using NLP for contextual data.

Hospital Invoice Extractor –
Extracted patient info, hospital details, date, cost, and diagnosis, fine-tuned for varied invoice layouts.

The API returned structured JSON outputs, enabling smooth integration with CRMs, claim portals, and EMR systems.

Tools & Tech Used

  • Framework: FastAPI
  • OCR: Tesseract, EasyOCR
  • NLP/Parsing: spaCy, regex
  • Preprocessing: PDFPlumber
  • Validation: Pydantic
  • Integrations: EMR systems

Results & Impact

  • 4 production-ready endpoints tailored to document types
  • 95%+ accuracy on clean scans and PDFs
  • 70% reduction in processing time compared to manual entry
  • Standardized JSON outputs for downstream automation
  • Improved accuracy in claim validations & medical record updates

Key Takeaways

  • OCR + NLP unlock automation in industries with complex, document-heavy workflows.
  • Standardized APIs accelerate claim verification and compliance in healthcare/insurance.
  • MoreYeahs ensures scalable, production-ready integrations for enterprise use cases.

Project Duration & Team

  • Duration: 8 weeks
  • Team: 1 API Developer, 1 Machine Learning Engineer (OCR/NLP), 1 QA/Test Automation Engineer
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