Amharic Character Recognition

Overview

A specialized optical character recognition (OCR) system for Amharic script, addressing the unique challenges of digitizing Ethiopia's official language. This system uses convolutional neural networks to detect and recognize Amharic characters from images with high accuracy, enabling digital preservation and processing of Amharic text documents.

Key Features

• Specialized Character Recognition: Precisely identifies the unique fidel (characters) of the Amharic script, including the core 33 character forms
• Comprehensive Image Preprocessing: Multi-stage pipeline including noise removal, thresholding, and contour detection to isolate characters
• Custom CNN Architecture: Tailored convolutional neural network designed specifically for Amharic script characteristics
• Efficient Character Segmentation: Advanced algorithms to separate connected characters common in handwritten Amharic
• Training Dataset Integration: System trained on carefully prepared datasets of Amharic characters

Technical Implementation

• Image Preprocessing: OpenCV-based pipeline for cleaning, cropping, and normalizing input images
• Model Architecture: Sequential CNN with multiple convolutional layers followed by dense classifications
• Data Preparation: Custom data processing workflow to handle the hierarchical nature of Amharic script
• Character Mapping: Comprehensive Unicode mapping system for Amharic fidel representation
• Model Evaluation: Robust testing framework to ensure recognition accuracy across varied inputs

Application Areas

• Digitization of historical Amharic documents and manuscripts
• Automated processing of Amharic forms and paperwork
• Assistive technology for visually impaired Amharic speakers
• Educational tools for Amharic language learning

Technical Challenges Overcome

• Handling the complex nature of Amharic script with its large character set
• Developing effective preprocessing techniques for varied document qualities
• Training deep learning models with limited available Amharic datasets
• Balancing model complexity with processing efficiency