Abstract

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.

中文翻译：

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预分段手写多语言字符的识别研究

摘要

为了识别包括阿萨姆语，孟加拉语，英语，古吉拉特语，印地语，马拉地语，旁遮普语，泰米尔语等各种语言的手写文本，已经进行了广泛的研究。在模式识别领域，多语言文本文档的识别仍然是一个挑战。在本文中，对用于识别由英语，印地语和旁遮普语组成的预分段多语言字符的各种特征和分类器进行了研究。在特征提取阶段，考虑了各种技术，即分区特征，对角线特征，基于水平峰值范围的特征以及基于交点和开放端点的特征。在分类阶段，尝试了三种不同的分类器，即k-NN，Linear-SVM和MLP。还已经执行了各种特征和分类器的不同组合。