Brahmi character recognition based on SVM (support vector machine) classifier using image gradient features

Abstract

Understanding the ancient script can provide rich details of a civilization, like its cultural, political and social scenarios. Brahmi, an ancient mother script, has been a key to the development of many modern Indian scripts like Gurmukhi, Devanagari, Bangla etc. Inferring ancient writings can be a tedious job and moreover, if executed manually, it requires several language experts. The current paper presents a recognition system for Brahmi characters using linear Support Vector machine classifier. Gradient information of the character images pixels is extracted, and histogram of the gradients is stored as a feature vector for each character image. Character dataset includes both handwritten character images and images from the internet. Linear SVM classifier is trained on the feature set of 24 images of each character. The proposed recognition system is performed with an accuracy of 91.6% to recognize the Brahmi characters from the test images.

Subject Classification:

• Pattern Recognition

Keywords:

• Brahmi Script
• Image Gradient
• Histogram of Oriented Gradient (HOG)
• Support Vector Machine (SVM)