Braille Character Recognition with Histogram of Oriented Gradients (HOG) and SVM-Based Image Processing

Abstract

This research explores the application of Support Vector Machine (SVM) to enhance Braille letter recognition through image processing, a critical technology aimed at improving information accessibility for the general public. Braille letters, as a vital tactile writing system for this community, are represented by patterns of dots interpreted through touch. By employing SVM method optimized using Grid Search, the study achieved an 82% accuracy in classifying Braille letter images from a test dataset of 1560 images. These results confirm SVM's effectiveness in recognizing and classifying Braille letters, validating the efficiency of this approach in Braille image classification. The implications of this research include significant contributions to technology supporting inclusivity and information accessibility, emphasizing the importance of structured and optimized systems in addressing the challenges of recognizing different characters in the Braille writing system.