Handwritten Bangla Alphabet Recognition using an MLP Based Classifier

TL;DR

This paper develops an MLP-based classifier utilizing a 76-element feature set for recognizing handwritten Bangla alphabets, achieving over 86% accuracy on training data and 75% on test data, aiding OCR system development.

Contribution

It introduces a novel feature set and applies an MLP classifier specifically for handwritten Bangla alphabet recognition, demonstrating promising recognition performance.

Findings

Recognition accuracy of 86.46% on training data

Recognition accuracy of 75.05% on test data

Potential application in Bangla OCR systems

Abstract

The work presented here involves the design of a Multi Layer Perceptron (MLP) based classifier for recognition of handwritten Bangla alphabet using a 76 element feature set Bangla is the second most popular script and language in the Indian subcontinent and the fifth most popular language in the world. The feature set developed for representing handwritten characters of Bangla alphabet includes 24 shadow features, 16 centroid features and 36 longest-run features. Recognition performances of the MLP designed to work with this feature set are experimentally observed as 86.46% and 75.05% on the samples of the training and the test sets respectively. The work has useful application in the development of a complete OCR system for handwritten Bangla text.