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Recognition of Similar Shaped Handwritten Characters Using Logistic Regression

机译:使用Logistic回归识别类似形状的手写字符

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Recognition of similar shaped characters is a difficult problem and in character recognition systems most of the errors occur in similar shaped characters. In this article we propose a generic method to differentiate between two similar shaped characters, which works well not only when the characters are rotated about its center, but also in the presence of noise. Rotation is taken care of by contour distance based approach and recognition is done based on logistic regression. We consider a training data set to estimate the parameters of the logistic model, and using these parameters we classify the test object. We have considered pairs of similar shape characters of Bengali script for testing our algorithm.
机译:对类似形状的角色的识别是一个难题,并且在字符识别系统中,大多数错误发生在类似的形状的字符中。 在本文中,我们提出了一种仿制方法来区分两个类似的形状的字符,这不仅适用于字符绕其中心旋转,而且在存在噪声时。 通过基于轮廓距离的方法照顾旋转,并且基于Logistic回归完成识别。 我们考虑一个培训数据设置以估计逻辑模型的参数,并使用这些参数来分类测试对象。 我们已经考虑了孟加拉脚本的类似形状字符,用于测试我们的算法。

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