OCRApp 📄🔍

Config-Driven OCR Pipeline for Structured Data Extraction

A desktop application that extracts structured data from images
using template-based OCR and automatically inserts it into a database.

Built with Python, Tesseract OCR, and a modular extraction engine.

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🎬 Demo

OCRApp_Demo.mp4

Example workflow:

Image → Preprocessing → OCR → Extraction → Validation → Database

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❗ Problem

Extracting structured information from documents is difficult because:

• OCR returns unstructured text
• Documents have different layouts
• Data must often be manually entered into databases

This project solves the problem by using template-driven OCR extraction.

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💡 Solution

OCRApp allows users to define templates describing:

• What data to extract
• Where it appears in the document
• How to validate it

The application then:

1. Runs OCR on the image
2. Extracts fields using configured methods
3. Validates the data
4. Inserts results directly into a database

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🏗 Architecture

OCRAPP
│
├── configs/        → OCR templates
├── data/           → extracted database
├── db/             → database layer
├── logs/           → application logs
├── ocr_core/       → OCR engine & extractors
├── ui/             → desktop interface
└── utils/          → shared utilities

Core Components

OCR Engine
Handles preprocessing and text extraction.

Extractors
Multiple extraction strategies:

• Bounding Box extraction
• Regex extraction
• Label-nearby detection
• Registry lookup

Template System
Allows users to configure extraction logic without modifying code.

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✨ Features

• Template-driven OCR extraction
• Multiple extraction methods
• Built-in preprocessing pipeline
• Data validation
• Automatic database insertion
• Editable results via UI
• Modular architecture

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🔎 Extraction Methods

OCRApp supports multiple extraction strategies:

Method	Description
bbox_region	Extract text from bounding box
regex	Extract using pattern matching
label_nearby	Detect value near label
registry	Lookup structured values

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⚙️ Installation

Clone the repository:

git clone https://github.com/yourname/ocrapp.git
cd ocrapp

Install dependencies:

pip install -r requirements.txt

Install Tesseract OCR

Windows:
https://github.com/UB-Mannheim/tesseract/wiki

Make sure the path is configured:

pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

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▶️ Run the Application

python app.py

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🧾 Template Example

{
  "fields": {
    "name": {
      "method": "label_nearby",
      "label": "Name"
    },
    "credits": {
      "method": "bbox_region",
      "bbox": [0.3,0.4,0.6,0.5]
    }
  }
}

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📚 What I Learned

• Designing modular Python project architectures
• Building config-driven systems using templates
• Implementing an OCR processing pipeline
• Improving OCR accuracy through image preprocessing
• Creating multiple extraction strategies (bbox, regex, label detection)
• Structuring applications into logical modules
• Integrating SQLite for structured data storage
• Implementing logging and debugging workflows

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🚀 Future Improvements

• PDF batch processing
• ML-based layout detection
• Automatic template generation
• REST API support
• Batch OCR processing for large datasets

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📜 License

This project is licensed under the MIT License.

MIT License

Copyright (c) 2026

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software.