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A weight learning technique for cursive handwritten text categorization with fuzzy confusion matirx

机译:基于模糊混淆矩阵的草书手写文本分类权重学习技术

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A fuzzy confusion matrix based cursive handwritten text categorization has been implemented. Printed text is obtained from handwritten text through Modified Optimal Clustering Algorithm (MOCA). Optimal Clustering Algorithm (OCA) groups texts into different subject categories. Learning is conducted to extract the attributes along with corresponding weights for each subjects. Fuzzy confusion matrix has been used to measure several performance metrics with Holdout method. These are satisfactory. Over and above the text learning and recognition time is very less making the system efficient also.
机译:基于模糊混淆矩阵的草书手写文本分类已实现。通过修改的最佳聚类算法(MOCA)从手写文本中获取印刷文本。最佳聚类算法(OCA)将文本分为不同的主题类别。进行学习以提取属性以及每个主题的相应权重。模糊混淆矩阵已用于使用Holdout方法测量多个性能指标。这些令人满意。除了文本学习和识别时间之外,该系统的效率也非常低。

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