Abstract
Although some encouraging progress has been achieved in handwritten Chinese character recognition (HCCR), handwritten Chinese address recognition (HCAR) remains an ongoing challenge. Few methods achieve satisfying performance on it due to more irregular distortion and overlapping between characters. In this paper, we first extract keywords from the address by a specially designed key character classifier. Then we use a single character network to recognize the place names. In order to take advantage of hierarchical relationships among place names, we construct an address database from the Chinese administrative divisions and design an error-correction method to improve the recognition of place names. Experiments on handwritten Chinese address datasets demonstrate the effectiveness of the proposed method.
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Fu, Q., Ding, X., Liu, C., Jiang, Y.: A hidden Markov model based segmentation and recognition algorithm for chinese handwritten address character strings. In: IEEE 8th International Conference on Document Analysis and Recognition, pp. 590–594 (2005)
Kim, I.J., Xie, X.: Handwritten hangul recognition using deep convolutional neural networks. Int. J. Doc. Anal. Recogn. 18(1), 1–13 (2015)
Liu, C.L., Yin, F., Wang, D.H., Wang, Q.F.: Chinese handwriting recognition contest 2010. In: Chinese Conference on Pattern Recognition, pp. 1–5 (2010)
Liu, C.L., Yin, F., Wang, D.H., Wang, Q.F.: Online and offline handwritten chinese character recognition: benchmarking on new databases. Patt. Recogn. 46(1), 155–162 (2013)
Shi, D., Gunn, S., Damper, R.: Handwritten chinese radical recognition using nonlinear active shape models. IEEE Trans. Patt. Anal. Mach. Intell. 25(2), 277–280 (2003)
Srihari, S.N., Yang, X., Ball, G.R.: Offline chinese handwriting recognition: an assessment of current technology. Front. Comput. Sci. China 1(2), 137–155 (2007)
Su, Y.M., Wang, J.F.: Decomposing chinese characters into stroke segments using SOGD filters and orientation normalization. In: International Conference on Pattern Recognition, vol. 2, pp. 351–354 (2004)
Wang, A.B., Fan, K.C.: Optical recognition of handwritten chinese characters by hierarchical radical matching method. Patt. Recogn. 34(1), 15–35 (2001)
Wang, C., Hotta, Y., Suwa, M., Naoi, S.: Handwritten chinese address recognition. In: International Workshop on Frontiers in Handwriting Recognition, pp. 539–544 (2004)
Wang, Q., Chi, Z., Feng, D.D., Zhao, R.: Hidden Markov random field based approach for off-line handwritten chinese character recognition. In: International Conference on Pattern Recognition, vol. 2, pp. 347–350 (2000)
Yin, F., Wang, Q.F., Zhang, X.Y., Liu, C.L.: ICDAR 2013 chinese handwriting recognition competition. In: IEEE 12th International Conference on Document Analysis and Recognition, pp. 1464–1470 (2013)
Zeng, J., Liu, Z.: Markov random fields for handwritten chinese character recognition. In: IEEE 8th International Conference on Document Analysis and Recognition, vol. 1, pp. 101–105 (2005)
Zhang, H., Guo, J., Chen, G., Li, C.: HCL2000 - a large-scale handwritten chinese character database for handwritten character recognition. In: IEEE 10th International Conference on Document Analysis and Recognition, pp. 286–290 (2009)
Zhang, X.Y., Bengio, Y., Liu, C.L.: Online and offline handwritten chinese character recognition: a comprehensive study and new benchmark. Patt. Recogn. 61, 348–360 (2017)
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Liu, Q., Wang, D., Lu, H., Li, C. (2018). Handwritten Chinese Character Recognition Based on Domain-Specific Knowledge. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_21
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DOI: https://doi.org/10.1007/978-3-030-00767-6_21
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