离线草书手写泰米尔字符识别

R. Kannan, R. Prabhakar, R. Suresh
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引用次数: 62

摘要

在光学字符识别方面,即使引入了新技术,手写仍然作为一种交流和记录信息的手段在日常生活中持续存在。隐马尔可夫模型(HMM)在语音识别领域取得成功后,一直是西方手写体识别领域的热门选择。然而,当涉及到印度文字识别时,已发表的使用hmm的工作是有限的,并且通常集中在孤立的字符识别上。提出了一种草书手写泰米尔文字的离线识别系统。在此基础上,提出了一种基于HMM并结合时域和频域特征的离线泰米尔语手写体识别系统。系统的容忍度是显而易见的,因为它可以克服由字体变化引起的复杂性,并且证明是灵活和健壮的。在一个综合数据库上实施这种方法,结果的准确性更高。这些初步结果是有希望的,值得在这个方向上进一步研究。研究结果也鼓舞了我们探索将这种方法应用于其他印度文字的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Off-line Cursive Handwritten Tamil Character Recognition
Concerning to optical character recognition, handwriting has sustained to persist as a means of communication and recording information in day to day life even with the introduction of new technologies. Hidden Markov Models (HMM) have long been a popular choice for Western cursive handwriting recognition following their success in speech recognition. However, when it comes to Indic script recognition, the published work employing HMMs is limited, and generally focused on isolated character recognition. A system for offline recognition of cursive handwritten Tamil characters is presented. In this effort, offline cursive handwritten recognition system for Tamil based on HMM and uses a combination of Time domain and frequency domain feature is proposed. The tolerance of the system is evident as it can overwhelm the complexities arise out of font variations and proves to be flexible and robust. Higher degree of accuracy in results has been obtained with the implementation of this approach on a comprehensive database. These initial results are promising and warrant further research in this direction. The results are also encouraging to explore possibilities for adopting the approach to other Indic scripts as well.
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