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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >Online Tibetan Handwriting Recognition for Large Character Set on New Databases
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Online Tibetan Handwriting Recognition for Large Character Set on New Databases

机译:新数据库中大字符集的在线藏文手写识别

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摘要

The online handwriting recognition of Tibetan characters is still in its infancy. For further research, an online handwriting database of large Tibetan character set was developed, and a recognition research was carried out on this database as a baseline result. The Northwest Minzu University Online Tibetan Handwriting Database (NMU-OLTHWDB) contains 7240 different types of characters, and the sample number in each type is 5000. The total number of samples is 7240 x 5000. The database covers Tibetan Character Collection, Information Technology Tibetan Coded Character set (Extension Set A), and Information Technology Tibetan Coded Character set (Extension Set B). The characters in the database are composed of 170 types of different components. We studied the online handwritten Tibetan recognition software also, and the character feature extraction, classifier training, and the statistics and analysis of the recognition results on the test set were mainly introduced. The character features included the direction attribute coefficients and spatial combination, and the feature matrix was compressed by Linear Discriminate Analysis (LDA). A quick classifier was designed by a modified quadratic discriminate function (QMQDF), and was trained with 4500 sets of samples. In the large character set, the recognition rates of top 1, top 3, top 5, and top 10 were 75.2%, 89.56%, 93.02%, and 95.96%, respectively. Moreover, an online handwriting recognition system for Tibetan large character set was designed with good performance.
机译:藏文字符的在线手写识别仍处于起步阶段。为了进一步研究,开发了一个大型藏文字符集的在线手写数据库,并对该数据库进行了识别研究,以此作为基线结果。西北民族大学在线藏文手写数据库(NMU-OLTHWDB)包含7240种不同类型的字符,每种类型的样本数量为5000。样本总数为7240 x5000。该数据库涵盖藏文字符集,信息技术藏文。编码字符集(扩展集A)和信息技术藏文编码字符集(扩展集B)。数据库中的字符由170种不同的成分组成。我们还研究了在线手写藏文识别软件,主要介绍了字符特征提取,分类器训练以及对测试集识别结果的统计和分析。字符特征包括方向属性系数和空间组合,并且特征矩阵通过线性判别分析(LDA)进行压缩。通过改进的二次判别函数(QMQDF)设计了一个快速分类器,并用4500套样本进行了训练。在大型字符集中,前1位,前3位,前5位和前10位的识别率分别为75.2%,89.56%,93.02%和95.96%。此外,设计了具有良好性能的在线藏文大字符集手写识别系统。

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