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Novel Curve Signatures and a Combination Method for Thai On-Line Handwriting Character Recognition

机译:新型曲线签名与泰式在线手写字符识别的组合方法

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There is no commercial character recognition software that supports Thai handwriting. Thai handwritten character recognition is needed to convert handwritten text written on mobile and tablet devices into computer encoded text. We propose a novel method that joins three curve signatures. The first signature is the normalized tangent angle function (TAF), which provides rough classification. The other two novel curve signatures are the relative position matrix (RPM), which is used to compare global curve features, and the straightened tangent angle function (STAF), which is used to compare the tangent angle along the cumulative unsigned curvature domain. In the recognition process, an input curve is extracted for these three signatures and the similarity against each character in the handwriting templates is measured. Then, the similarity scores are weighted and summed for ranking. Our experiment is done on 48 handwriting sample sets (44 Thai consonants appear in each set, and there are 4 sets per handwriting). Our methods yield an accuracy of 94.08% for personal handwriting, and 92.23% for general handwriting.
机译:没有支持泰语手写的商业字符识别软件。需要泰语手写的字符识别,以将手写文本转换为在移动设备和平板电脑设备上写入计算机编码的文本。我们提出了一种加入三个曲线签名的新方法。第一个签名是归一化的切线角函数(TAF),提供粗略分类。另外两个新颖的曲线签名是相对位置矩阵(RPM),其用于比较全局曲线特征,以及用于比较沿着累积的无符号域的切线角度的拉直切线角度(STAF)。在识别过程中,测量对这三个签名的输入曲线,并测量对手写模板中的每个字符的相似性。然后,相似性分数加权和总共进行排名。我们的实验是在48个手写样本组上完成的(每组44个泰语辅音,每个手写有4套)。我们的方法为个人手写的准确度为94.08%,一般笔迹92.23%。

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