International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 3, No. 2, 8 Mar. 2011
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A Stroke Shape and Structure Based Approach for Off-line Chinese Handwriting Identification
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Author(s)
Jun Tan
1,*
Jian-Huang Lai
1
Chang-Dong Wang
1
Ming-Shuai Feng
2
Index Terms
Handwriting identification, off-line, Chinese character, stroke, mathematical morphology, feature extraction
Abstract
Handwriting identification is a technique of automatic person identification based on the personal handwriting. It is a hot research topic in the field of pattern recognition due to its indispensible role in the biometric individual identification. Although many approaches have emerged, recent research has shown that off-line Chinese handwriting identification remains a challenge problem. In this paper, we propose a novel method for off-line Chinese handwriting identification based on stroke shapes and structures. To extract the features embedded in Chinese handwriting characters, two special structures have been explored according to the trait of Chinese handwriting characters. These two structures are the bounding rectangle and the TBLR quadrilateral. Sixteen features are extracted from the two structures, which are used to compute the unadjusted similarity, and the other four commonly used features are also computed to adjust the similarity adaptively. The final identification is performed on the similarity. Experimental results on the SYSU and HanjaDB1 databases have validated the effectiveness of the proposed method.
Cite This Paper
Jun Tan, Jian-Huang Lai, Chang-Dong Wang, Ming-Shuai Feng,"A Stroke Shape and Structure Based Approach for Off-line Chinese Handwriting Identification", International Journal of Intelligent Systems and Applications(IJISA), vol.3, no.2, pp.1-8, 2011. DOI: 10.5815/ijisa.2011.02.01
Reference
[1] Z. Y. He, B. Fang, J. W. Du, Y. Y. Tang, and X. You, “A novel method for off-line handwriting-based writer identification,” in Proc. of the 8th Int. Conf. on Document Analysis and Recognition, 2005.
[2] M. Bulacu and L. Schomaker, “Text-independent writer identification and verification using textural and allographic features,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 4, pp. 701–717, April 2007.
[3] G. Zhu, Y. Zheng, D. Doermann, and S. Jaeger, “Signature detection and matching for document image retrieval,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 11, pp. 2015– 2031, Nov. 2009.
[4] M. Panagopoulos, C. Papaodysseus, P. Rousopoulos, D. Dafi, and S. Tracy, “Automatic writer identification of ancient greek inscriptions,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 8, pp. 1404–1414, Aug. 2009.
[5] T. Wang, Z. Chen, W. Li, X. Huang, P. Chen, and S. Zhu, “Relationship between personality and handwriting of Chinese characters using artificial neural network,” in Proc. of the 1st Int. Conf. on Information Engineering and Computer Science, 2009.
[6] Z. Y. He and Y. Y. Tang, “Chinese handwriting-based writer identification by texture analysis,” in Proc. of the 3rd Int. Conf. on Machine Learning and Cybemetics, 2004.
[7] Y. Zhu, T. Tan, and Y. Wang, “Biometric personal identification based on handwriting,” in Proc. of the 15th Int. Conf. on Pattern Recognition, 2000.
[8] Q. Chen, Y. Yan, W. Deng, and F. Yuan, “Handwriting identification based on constructing texture,” in Proc. of the 1st Int. Conf. on Intelligent Networks and Intelligent Systems, 2009.
[9] Z. Y. He, Y. Y. Tang, and X. You, “A contourlet-based method for writer identification,” in Proc. of Int. Conf. on Systems, Man and Cybernetics, 2005.
[10] M. Bulacu, L. Schomaker, and L. Vuurpijl, “Writer identification using edge-based directional features,” in Proc. of the 7th Int. Conf. on Document Analysis and Recognition, 2003.
[11] A. Schlapbach and H. Bunke, “Off-line handwriting identification using hmm based recognizers,” in Proc. of the 17th Int. Conf. on Pattern Recognition, 2004.
[12] A. H.-R. Ko, P. R. Cavalin, R. Sabourin, and A. de Souza Britto Jr., “Leave-one-out-training and leave-one-out-testing hidden markov models for a handwritten numeral recognizer: The implications of a single classifier and multiple classifications,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 12, pp. 2168–2178, Dec. 2009.
[13] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed. Prentice Hall, 2002.
[14] J. Tan, J.-H. Lai, C.-D. Wang and M.-S. Feng, “Off-line Chinese Handwriting Identification Based on Stroke Shape and Structure,” in Proc. Of the 2nd Int. Conf. on Inf. Engineering and Computer Science, 2010.
[15] E. N. Zois and V. Anastassopoulos, “Morphological waveform coding for writer identification,” Pattern Recognition, vol. 33, pp.385-398, 2000.
[16] W. Y. Leng and S. M. Shamsuddin, “Writer identification for Chinese handwriting,” Int. J. Advance. Soft Comput. Appl., vol. 2, no. 2, July, 2010.
[17] F. H. Cheng, “Multi-stroke relaxation matching method for handwritten Chinese character recognition,” Pattern Recognition. Vol. 31, no. 4, pp.401-410, 1998.
[18] X. Li and X. Ding, “Writer Identification of Chinese Handwriting Using Grid Microstructure Feature,” ICB, 2009.
[19] http://ai.kaist.ac.kr/Resource/dbase/KAIST-DB.htm#Hanja