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A hidden Markov model based segmentation and recognition algorithm for Chinese handwritten address character strings

机译:基于隐马尔可夫模型的中文手写地址字符串分割与识别算法

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摘要

An efficient method of Chinese handwritten address character string segmentation and recognition is presented. First, an address string image is presegmented into several radicals using stroke extraction and stroke mergence. Next, the radical series obtained by presegmentation merge into different character image series according to different merging paths. After that, the optimal merging path is selected using recognition and semantic information. The recognition information is given by the character classifier. The semantic information is obtained from large scale address database containing more than one hundred thousand address items. Finally, the optimal recognition results of the character image series which are combined by radical series according to the optimal merging paths are obtained. In experiments on 897 mail images, the proposed method achieves correct rate of 85 percent while the error rate is 15 percent.
机译:提出了一种有效的中文手写地址字符串分割与识别方法。首先,使用笔划提取和笔划合并将地址字符串图像预先分段为多个部首。接下来,通过预分割得到的部首系列根据不同的合并路径合并为不同的字符图像系列。之后,使用识别和语义信息选择最佳合并路径。识别信息由字符分类器给出。语义信息是从包含十万多个地址项的大规模地址数据库中获得的。最终,获得字符图像序列的最优识别结果,并根据最优合并路径将其与部首序列组合。在897幅邮件图像的实验中,提出的方法的正确率为85%,错误率为15%。

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