Support Vector Machine for Handwritten Character Recognition

TL;DR

This paper presents a system using Support Vector Machine (SVM) for recognizing unconstrained handwritten Malayalam characters, achieving over 92% accuracy with a specific feature set and a large character database.

Contribution

It introduces a novel application of SVM with a combined feature set for Malayalam handwriting recognition, demonstrating high accuracy on a substantial dataset.

Findings

Achieved 92.24% recognition accuracy

Utilized 64 local and 4 global features

Applied to a database of 10,000 samples

Abstract

Handwriting recognition has been one of the most fascinating and challenging research areas in field of image processing and pattern recognition. It contributes enormously to the improvement of automation process. In this paper, a system for recognition of unconstrained handwritten Malayalam characters is proposed. A database of 10,000 character samples of 44 basic Malayalam characters is used in this work. A discriminate feature set of 64 local and 4 global features are used to train and test SVM classifier and achieved 92.24% accuracy