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基于人工神经网络的非相关字符识别

Non-Correlated Character Recognition using Artificial Neural Network

Veer Surendra Sai University of Technology（维尔·苏伦德拉·赛科技大学）；National Institute of Science and Technology（国家科学技术学院）

Tirtharaj Dash, Tanistha Nayak

备注：
appeared in: proceedings of National Conference on Dynamics and Prospects of Data Mining: Theory and Practices (DPDM)-2012; September 30, 2012, India; Publisher: OITS-BLS, Balasore Chapter; Proceeding ISBN: 987-93-81361-31-6, pp. 79-83
发表信息：
proc. National Conference on Dynamics and Prospects of Data Mining: Theory and Practices (DPDM)-2012; September 30, 2012, India; ISBN: 987-93-81361-31-6, pp. 79-83
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摘要：

研究了一种基于人工神经网络的手写体英文字符识别方法。该工作是在离线环境下针对不相关的字符进行的，这些字符之间不具有任何线性关系。我们测试特定的测试字符是否属于一个集群。在Matlab环境下进行了实现，并成功地进行了测试。用52组英文字母对神经网络进行训练和测试。用26个大写字母和26个小写字母对算法进行了测试。测试结果表明，提出的基于人工神经网络的算法表现出85%的最高识别率。

英文摘要：

This paper investigates a method of Handwritten English Character Recognition using Artificial Neural Network (ANN). This work has been done in offline Environment for non correlated characters, which do not possess any linear relationships among them. We test that whether the particular tested character belongs to a cluster or not. The implementation is carried out in Matlab environment and successfully tested. Fifty-two sets of English alphabets are used to train the ANN and test the network. The algorithms are tested with 26 capital letters and 26 small letters. The testing result showed that the proposed ANN based algorithm showed a maximum recognition rate of 85%.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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