Advanced Search
Volume 30 Issue 12
Jan.  2011
Turn off MathJax
Article Contents
Fu Qiang, Ding Xiao-Qing, Jiang Yan. Segmentation and Recognition Algorithm for Chinese Handwritten Address Character String[J]. Journal of Electronics & 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 & Information Technology, 2008, 30(12): 2916-2920. doi: 10.3724/SP.J.1146.2007.00961

Segmentation and Recognition Algorithm for Chinese Handwritten Address Character String

doi: 10.3724/SP.J.1146.2007.00961
  • Received Date: 2007-06-15
  • Rev Recd Date: 2007-10-16
  • Publish Date: 2008-12-19
  • An effective segmentation and recognition method of Chinese handwritten address strings is proposed. Firstly, over-segmentation is applied to character string images by extracting stroke and merging them to obtain radical sequences. Next, the optimal segmentation and recognition result is achieved by synthesizing geometric analysis, isolated character classifier and semantic information together. The geometric information is estimated on current character string to adapt to various writing styles of character strings. The isolated character classifier adopts MQDF classifier with ten candidate results and their confidence. The semantic information is described by a character-based bi-gram model, parameters of which are estimated from a database containing 180,000 addresses items. The algorithm is tested on 3,000 actual handwritten address samples and the single-character recognition rate is 88.28%.
  • loading
  • [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
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (3199) PDF downloads(1319) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return