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OCR-Based Identity Verification — Technical Proof of Concept

OCR-Based Identity Verification — Technical Proof of Concept

OCR Identity Pipeline – Secure ID Verification

Python-based proof of concept solving real-world OCR challenges: image preprocessing, data extraction accuracy, and error handling for government ID verification workflows

Python-based proof of concept solving real-world OCR challenges: image preprocessing, data extraction accuracy, and error handling for government ID verification workflows.

From ID photo to verified record — managed OCR → field checks → secure storage (RDS/Postgres, Redshift).


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Impact & Deliverables

  • Working Flask API — Built functional REST endpoint accepting image uploads and returning structured JSON with extracted ID data (name, DOB, ID number)

  • Image preprocessing pipeline — Implemented contrast adjustment, grayscale conversion, and noise reduction to improve OCR accuracy on real-world photos with poor lighting or angles

  • Data extraction & validation — Developed parsing logic to extract structured fields from raw OCR output, with error detection for missing or malformed data

  • Open-source demo — Published complete codebase on GitHub with documentation, making it easy to run locally or adapt for production use cases

Workflow

Workflow

Flask API
Image Preprocessing
OCR Extraction
Field Parsing
Error Detection
Structured Output
Flask APIImage PreprocessingOCR ExtractionField ParsingError DetectionStructured Output
Flask APIImage PreprocessingOCR ExtractionField ParsingError DetectionStructured Output

Consulting Relevance

This pipeline reduces manual entry and compliance risk by automating ID validation and secure data storage. It supports use cases in healthcare, onboarding, fintech, and other regulated industries where reliable identity verification is critical.

Technical Implementation
Flask API architecture with OCR engine integration and data validation pipeline
API Layer
Flask REST Endpoint
POST /verify accepts multipart image uploads, returns JSON with extracted fields or error messages.
Request Handling
File validation, size limits, supported formats (JPG, PNG); error responses for invalid inputs.
CORS & Security Headers
Configured for local testing and demo deployment scenarios.
Processing Pipeline
Image Preprocessing
Grayscale Conversion
Reduces color noise that can confuse OCR extraction on government IDs.
Contrast Enhancement
Improves text clarity on photos with poor lighting or faded documents.
Noise Reduction
Filters out artifacts from phone camera compression or scanning imperfections.
OCR & Data Extraction
OCR Engine
Open-source OCR processes preprocessed image, outputs raw text with confidence scores.
Field Parsing Logic
Pattern matching and regex extract structured fields (name, DOB, ID number) from unstructured OCR output.
Validation Rules
Checks for required fields, date formats, ID number patterns; flags suspicious or incomplete extractions.
Output & Deployment
JSON Response Format
Structured output with extracted fields, confidence levels, and error indicators for easy integration.
GitHub Repository
Complete codebase with README, requirements.txt, and sample images for local testing.
Docker Support (optional)
Containerized deployment for consistent environment across development and production.
Technology Stack
Python 3.8+, Flask framework, open-source OCR engine, OpenCV for image processing, PIL/Pillow for image handling, pytest for testing suite.
Technical Implementation
Flask API architecture with OCR engine integration and data validation pipeline
API Layer
Flask REST Endpoint
POST /verify accepts multipart image uploads, returns JSON with extracted fields or error messages.
Request Handling
File validation, size limits, supported formats (JPG, PNG); error responses for invalid inputs.
CORS & Security Headers
Configured for local testing and demo deployment scenarios.
Processing Pipeline
Image Preprocessing
Grayscale Conversion
Reduces color noise that can confuse OCR extraction on government IDs.
Contrast Enhancement
Improves text clarity on photos with poor lighting or faded documents.
Noise Reduction
Filters out artifacts from phone camera compression or scanning imperfections.
OCR & Data Extraction
OCR Engine
Open-source OCR processes preprocessed image, outputs raw text with confidence scores.
Field Parsing Logic
Pattern matching and regex extract structured fields (name, DOB, ID number) from unstructured OCR output.
Validation Rules
Checks for required fields, date formats, ID number patterns; flags suspicious or incomplete extractions.
Output & Deployment
JSON Response Format
Structured output with extracted fields, confidence levels, and error indicators for easy integration.
GitHub Repository
Complete codebase with README, requirements.txt, and sample images for local testing.
Docker Support (optional)
Containerized deployment for consistent environment across development and production.
Technology Stack
Python 3.8+, Flask framework, open-source OCR engine, OpenCV for image processing, PIL/Pillow for image handling, pytest for testing suite.

Open to short consults and build sprints.

Book a 15-min consult

© 2022–2025 Matt Tunison. All rights reserved.

About

Home

Book a 15-min consult

Open to short consults and build sprints.

Home

© 2022–2025 Matt Tunison. All rights reserved.

About