An Approach towards Malayalam Handwriting Recognition Using Dissimilar Classifiers. (2016)

• Record Type: Journal Article
• Authors: Alex, Meenu; Das, Smija
• Is Part Of: Procedia technology. Volume 25(2016)
• Page Start: 224
• Page End: 231
• Publication Date: 2016
• Subjects: Handwriting Recognition -- Segmentation -- SURF -- Curvature feature -- SVM -- Neural Network -- Ensemble learning; Technology -- Congresses; Technology -- Periodicals; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Technology; Conference proceedings; Periodicals; 605
• DOI: 10.1016/j.protcy.2016.08.101
• Languages: English
• ISSNs: 2212-0173

Abstract

Abstract: The handwriting recognition in Malayalam is a challenging as well as emerging area of pattern recognition. It is a tedious process mainly due to its enormous character set. Here we propose a novel method for handwriting recognition by using two dissimilar classifiers. It can also be called as an ensemble method in which multiple classifiers are combined to solve a particular problem and thereby improve the performance of the system. The experiment is conducted in 2 phases. In the first phase, 33 isolated characters in Malayalam were used. In the second phase, Malayalam sentences were used. From the preprocessed image, we were extracted two features: SURF feature and Curvature feature. These features were fed as input to a neural network and an SVM classifier. Finally, the result of both the classifiers was combined to get the final results. The system showed an accuracy of 89.2% in the first phase. An accuracy of 81.1% was exhibited in the second phase.