Support Vector Machine for Handwritten Character Recognition
This work addresses the challenge of automating handwriting recognition for the Malayalam script, which is an incremental improvement in a domain-specific application.
The paper tackled the problem of recognizing unconstrained handwritten Malayalam characters using a Support Vector Machine (SVM) classifier, achieving an accuracy of 92.24% on a dataset of 10,000 samples across 44 characters.
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