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Multi-stroke relaxation matching method for handwritten Chinese character recognition

机译:手写汉字识别的多笔松弛匹配方法

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Since Chinese characters can be represented by a set of basic line segments called sub-strokes, sub-strokes are often used as features to recognize handwritten Chinese Characters. However, the number of sub-strokes needed to evaluate a character are different due to handwriting variations and variations in the stroke extraction process. These differences in representation of the same character lower character recognition rate. A preliminary method to solve this problem is to merge several sub-strokes into a complete stroke. However, it is difficult to merge several sub-strokes into a correct and complete stroke because the Chinese character is not known in advance. In this paper, we propose a multi-stroke relaxation matching method to solve this problem. The proposed matching method can be divided into two parts; one is the multi-stroke relaxation process and the other is the multi-stroke select-match-pair process. The multi-stroke relaxation process will determine the optimal matching relations from the probability of each possible matching pair of sub-strokes and allow more than one of the sub-strokes to match with one or more additional sub-strokes by combining the merging steps into the relaxation process. The multi-stroke select-match-pair process is used to determine the stroke matching relation between the input and reference characters. Some experiments will be conducted to show the feasibility and correctness of the proposed algorithm. From the experimental results, we will prove that the proposed algorithm can solve the matching problem of different numbers of sub-strokes caused by handwriting variations and the stroke extraction process. For 2000 daily used Chinese characters, the actual recognition rate is 93.8% and the cumulative recognition rate of the first five candidates is 98.9%. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. [References: 40]
机译:由于汉字可以用称为子笔划的一组基本线段表示,因此子笔划通常用作识别手写汉字的特征。但是,由于笔迹的变化和笔划提取过程中的变化,评估字符所需的子笔划数是不同的。同一字符表示中的这些差异会降低字符识别率。解决此问题的一种初步方法是将几个子笔划合并为一个完整笔划。但是,由于预先不知道中文字符,因此很难将几个子笔划合并为正确且完整的笔划。在本文中,我们提出了一种多行程松弛匹配方法来解决该问题。提出的匹配方法可以分为两部分:一个是多行程放松过程,另一个是多行程选择匹配对过程。多笔画松弛过程将从每个可能的子笔画对对的概率中确定最佳匹配关系,并通过将合并步骤合并为一个或多个其他子笔画来允许多个子笔画与一个或多个其他子笔画进行匹配。放松的过程。多笔画选择匹配对过程用于确定输入字符和参考字符之间的笔画匹配关系。将进行一些实验,以证明该算法的可行性和正确性。从实验结果可以证明,该算法可以解决手写变化和笔画提取过程引起的不同数量的子笔画的匹配问题。对于2000个日常使用的汉字,实际识别率为93.8%,前五个候选者的累积识别率为98.9%。 (C)1998模式识别学会。由Elsevier Science Ltd.出版。保留所有权利。 [参考:40]

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