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基于多信息融合的中文手写地址字符串切分与识别

付强 丁晓青 蒋焰

付强, 丁晓青, 蒋焰. 基于多信息融合的中文手写地址字符串切分与识别[J]. 电子与信息学报, 2008, 30(12): 2916-2920. doi: 10.3724/SP.J.1146.2007.00961
引用本文: 付强, 丁晓青, 蒋焰. 基于多信息融合的中文手写地址字符串切分与识别[J]. 电子与信息学报, 2008, 30(12): 2916-2920. doi: 10.3724/SP.J.1146.2007.00961
Fu Qiang, Ding Xiao-Qing, Jiang Yan. Segmentation and Recognition Algorithm for Chinese Handwritten Address Character String[J]. Journal of Electronics and Information Technology, 2008, 30(12): 2916-2920. doi: 10.3724/SP.J.1146.2007.00961
Citation: Fu Qiang, Ding Xiao-Qing, Jiang Yan. Segmentation and Recognition Algorithm for Chinese Handwritten Address Character String[J]. Journal of Electronics and Information Technology, 2008, 30(12): 2916-2920. doi: 10.3724/SP.J.1146.2007.00961

基于多信息融合的中文手写地址字符串切分与识别

doi: 10.3724/SP.J.1146.2007.00961
基金项目: 

国家自然科学基金(60472002)和西门子公司合作项目(20030829- 24022SI202)资助课题

Segmentation and Recognition Algorithm for Chinese Handwritten Address Character String

  • 摘要: 该文提出了一种有效的中文手写地址字符串的切分与识别方法。首先,利用笔划提取与笔划合并将字符串图像进行过切分,得到字根图像序列;然后综合利用几何信息、识别信息和语义信息挑选最优的字根合并路径,得到最优的切分结果及对应的最优识别结果。其中,几何信息是根据当前字符串自身的特点统计得到,因此可适应不同书写风格的字符串。识别信息由单字分类器给出,包括10个候选识别结果及其相应的置信度;单字分类器采用MQDF分类器。语义信息用基于字的bi-gram模型进行描述,模型参数是从包含18万条地址数据的数据库中统计得到的。用3000个实际的手写地址样本做试验,单字识别正确率达到88.28%。
  • [1] Chiang C C and Yu S S. An iterative character segmentationmethod for irregularly formatted Chinese documents.Proceedings of the Optical Character Recognition andDocument Analysis, Taiwan, 1996: 61-67. [2] Lu Y and Shridhar M. Character segmentation inhandwritten words-an overview[J].Pattern Recognition.1996,29(1):77-96 [3] Arica N and Yarman-Vural F T. An overview of characterrecognition focused on off-line handwriting[J].IEEE Trans. onSystems, Man, and Cybernetics-Part C: Applications andReviews.2001, 31(2):216-233 [4] Casey R G and Lecolinet E. A survey of methods andstrategies in character segmentation[J].IEEE Trans. onPattern Analysis and Machine Intelligence.1996, 18(7):690-706 [5] Liu C L, Koga M and Fujisawa H. Lexicon-drivensegmentation and recognition of handwritten characterstrings for Japanese address reading[J].IEEE Trans. on PatternAnalysis and Machine Intelligence.2002, 24(11):1425-1437 [6] Tseng L Y and Chuang C T. An efficient knowledge basedstroke extraction method for multi-font Chinese characters[J].Pattern Recognition.1992, 25(12):1445-1458 [7] Tseng L Y and Chen R C. Segmenting handwritten Chinesecharacters based on heuristic merging of stroke boundingboxes and dynamic programming[J].Pattern RecognitionLetters.1998, 19(10):963-973 [8] 王嵘, 丁晓青, 刘长松. 基于笔划合并的手写体信函地址汉字切分识别. 清华大学学报(自然科学版), 2004, 44(4): 498-502.Wang R, Ding X Q and Liu C S. Handwritten Chineseaddress segmentation and recognition based on mergingstrokes. J of Tsinghua Univ. (Sci Tech), 2004, 44(4):498-502. [9] Fu Q, Ding X Q, and Liu C S, et al.. A hiddern Markov modelbased segmentation and recognition algorithm for Chinesehandwritten address character strings. InternationalConference on Document Analysis and Recognition, Seoul,Korea, 2005: 590-594. [10] Duda R O.[J].Hart P E and Stork D G. Pattern Classification.Second Edition, New York, John Wiley Sons Inc.2000,:- [11] Kimura F, Takashina K, and Tsuruoka S, et al.. Modifiedquadratic discriminant functions and its application toChinese character recognition[J].IEEE Trans. on PatternAnalysis and Machine Intelligence.1987, 9(1):149-153
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出版历程
  • 收稿日期:  2007-06-15
  • 修回日期:  2007-10-16
  • 刊出日期:  2008-12-19

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