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Shape descriptors based handwritten character recognition engine with application to Kannada characters

机译:基于形状描述符的手写字符识别引擎及其在卡纳达语字符中的应用

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In this paper, we discuss the implementation of shape based features, namely, Fourier descriptors and chain codes, for performing optical character recognition of binary images with application to Kannada handwritten characters. Invariant Fourier descriptors and normalized chain codes are obtained as features from preprocessed Kannada character binary images. Well known SVM classifier is used for recognition purpose. As an initial step towards recognition of handwritten characters, we have performed experiments on handwritten Kannada character numerals and vowels. The result computation is done using five-fold cross validation. The mean performance of the recognition system with the two shape based features together is 98.45% and 93.92%, for numeral characters and vowels, respectively. Further, the mean recognition rate of 95% is obtained for both vowels and characters taken together.
机译:在本文中,我们讨论了基于形状的特征(即傅立叶描述符和链码)的实现,该特征用于将二值图像应用于光学字符识别,并将其应用于卡纳达语手写字符。从预处理的卡纳达语字符二进制图像中获得不变傅立叶描述符和归一化链码作为特征。众所周知的SVM分类器用于识别目的。作为识别手写字符的第一步,我们对手写的卡纳达语字符数字和元音进行了实验。使用五重交叉验证完成结果计算。对于数字字符和元音,具有两个基于形状的特征的识别系统的平均性能分别为98.45%和93.92%。此外,元音和字符合在一起获得的平均识别率达到95%。

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