yujansaya/raha_beach_ocr

OCR Technical Assignment

OCR Text Extraction Project

Overview

This project extracts text from a set of 75 images using three different Optical Character Recognition (OCR) tools: EasyOCR, PyTesseract, and TrOCR. The results are then analyzed to identify discrepancies, and a consensus text is determined and corrected for common OCR errors. The final output is saved as a JSON file.

Table of Contents

• Project Structure
• Dependencies
• Installation
• Usage
• Error Handling
• Performance Optimization
• Output Quality
• Logging
• Future Improvements

Project Structure

.
├── selected_images/            # Directory containing the 75 images
├── ocr_results.json            # Output JSON file with extracted and corrected text
├── ocr_processing.log          # Log file for tracking processing details and errors
├── main.py                     # Main script for executing OCR and text processing
└── README.md                   # This readme file

Dependencies

The project requires the following libraries:

• Pillow (PIL)
• easyocr
• pytesseract
• transformers (for TrOCR)
• torch
• concurrent.futures
• tqdm
• Levenshtein (for choosing the best text among 3 models)

Ensure you have the required tools installed:

• Tesseract OCR: Tesseract Installation Guide
• Python 3.8 or higher

Installation

1. Clone the repository:

   git clone https://github.com/yujansaya/raha_beach_ocr/
   cd <your repository-directory>

2. Install the Python dependencies:

   pip install -r requirements.txt

3. Ensure Tesseract OCR is installed:

   • On Linux: sudo apt-get install tesseract-ocr
   • On macOS: brew install tesseract
   • On Windows: Download the Tesseract installer

Usage

1. Prepare the input images:

   • Place the 75 images in the selected_images/ directory.

2. Run the main script:

   python main.py

3. View the results:

   • The extracted and corrected text will be saved in ocr_results.json.
   • Logs can be found in ocr_processing.log.

Example Command

python main.py

Error Handling

The script includes comprehensive error handling:

• Logging Errors: Errors encountered during OCR processing are logged in ocr_processing.log.
• Handling Missing Files: Unsupported file formats or missing files are logged and skipped.

Performance Optimization

To improve performance:

• Parallel Processing: Uses ThreadPoolExecutor for concurrent processing of images.
• Batch Processing: Processes images in batches for TrOCR to utilize GPU more efficiently.

Output Quality

The preprocess_image function pre-propcesses the images that have very thick and bog letters that typically Tesseract fails to detect.

Selecting Best text among 3 models

The majority_vote, select_best_text and similarity functions compare the putpust of 3 models, and choose the best answer using Levenshtein Distance.

Logging

The script logs detailed information about the processing steps and errors:

• Log File: ocr_processing.log
• Log Levels: Includes information and error levels.

Future Improvements

• Model Fine-Tuning: Fine-tune OCR models on a dataset similar to the target images for better accuracy.
• Additional OCR Tools: Explore other OCR tools to enhance text extraction quality like Google Cloud Vision API, Amazon Textract or OpenCV.
• Advanced Error Correction: Implement machine learning models for context-based error correction.