A Study on Recognition of Pre-segmented Handwritten Multi-lingual Characters

Wide research has been carried out for recognition of handwritten text on various languages that include Assamese, Bangla, English, Gujarati, Hindi, Marathi, Punjabi, Tamil etc. Recognition of multi-lingual text documents is still a challenge in the pattern recognition field. In this paper, a study of various features and classifiers for recognition of pre-segmented multi-lingual characters consisting of English, Hindi and Punjabi has been presented. In feature extraction phase, various techniques, namely, zoning features, diagonal features, horizontal peak extent based features and intersection and open end point based features are considered. In classification phase, three different classifiers, namely, k-NN, Linear-SVM, and MLP are attempted. Different combinations of various features and classifiers have been also performed. For script identification, we have achieved maximum accuracy of 92.89% using a combination of Linear-SVM, k-NN, and MLP classifiers, and for character recognition of English, Hindi and Punjabi, we have achieved a recognition accuracy of 92.18%, 84.67% and 86.79%, respectively.