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Received: 2003-05-07

Revision Accepted: 2003-08-18

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Bio-Design and Manufacturing  2022 Vol.5 No.11 P.1392~1397

10.1631/jzus.2004.1392


An approach to offline handwritten Chinese character recognition based on segment evaluation of adaptive duration


Author(s):  LI Guo-hong, SHI Peng-fei

Affiliation(s):  Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China

Corresponding email(s):   lgh0929@sjtu.edu.cn

Key Words:  Handwritten Chinese character, Segmentation boundary, Segment, Duration


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LI Guo-hong, SHI Peng-fei. An approach to offline handwritten Chinese character recognition based on segment evaluation of adaptive duration[J]. Journal of Zhejiang University Science D, 2022, 5(11): 1392~1397.

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Abstract: 
This paper presents a methodology for off-line handwritten Chinese character recognition based on mergence of consecutive segments of adaptive duration. The handwritten Chinese character string is partitioned into a sequence of consecutive segments, which are combined to implement dissimilarity evaluation within a sliding window whose durations are determined adaptively by the integration of shapes and context of evaluations. The average stroke width is estimated for the handwritten Chinese character string, and a set of candidate character segmentation boundaries is found by using the integration of pixel and stroke features. The final decisions on segmentation and recognition are made under minimal arithmetical mean dissimilarities. Experiments proved that the proposed approach of adaptive duration outperforms the method of fixed duration, and is very effective for the recognition of overlapped, broken, touched, loosely configured Chinese characters.

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Reference

[1] Favata, J.T., 2001. Offline general handwritten word recognition using an approximate BEAM matching algorithm. IEEE Trans. on Pattern Recognition and Machine Intelligence, 23(9):1009-1021.

[2] Kahan, S., Pavlidis, T., Baird, H.S., 1987. On the recognition of printed characters of any fonts and sizes. IEEE Trans. on Pattern Recognition and Machine Intelligence, 9(2):274-288.

[3] Kim, G., Govindaraju, V., 1997. A lexicon driven approach to handwritten word recognition for real-time applications. IEEE Trans. on Pattern Recognition and Machine Intelligence, 19(4):366-379.

[4] Lee, S.W., Lee, D.J., 1996. A new methodology for gray-scale character segmentation and recognition. IEEE Trans. on Pattern Recognition and Machine Intelligence, 18(10):1045-1050.

[5] Liang, S., Shridhar, M., Ahmadi, M., 1994. Segmentation of touching characters in printed document recognition. Pattern Recognition, 27(6):825-840.

[6] Nafiz, A., Fatos, T.Y., 2002. Optical character recognition for cursive handwriting. IEEE Trans. on Pattern Recognition and Machine Intelligence, 24(6):801-813.

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