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Chinese Handwriting Recognition: An Algorithmic Perspective pp 1–21Cite as

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Abstract

Chinese character recognition, as an attractive toolset for Chinese digital library projects, has been drawing intense attention. The Chinese character system is used for communication and has served various political purposes in China, having played an important role in the development of Chinese civilization for over 3000 years. Thus, there are a large number of invaluable archives and documents recorded in Chinese, awaiting conversion as readable text for worldwide sharing. As an indispensable branch, Chinese handwriting recognition has been viewed as one of the most difficult pattern recognition tasks that pose its own unique challenges, such as huge variations in strokes, diversity of writing styles, and a large set of confusable categories. With ever-increasing amounts of training data, researchers have been developing effective algorithms to discern characters from different categories and compensate for the sample variations within the same category. With the help of their efforts, substantial achievements have been made in the field of Chinese handwriting recognition. In this book, essential algorithms for effective Chinese handwriting recognition are presented.

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References

  • L. Bahl, P. Brown, P. Souza, R. Mercer, Maximum mutual information estimation of hidden Markov model parameters for speech recognition, in IEEE International Conference on Acoustics, Speech and Signal Processing (1986)

    Google Scholar 

  • L.E. Baum, J.A. Eagon, An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology. Bull. Am. Math. Soc. 73, 360–363 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  • L.E. Baum, T. Petrie, Statistical inference for probabilistic functions of finite state Markov chains. Ann. Math. Stat. 37(6), 1554–1563 (1966)

    Article  MathSciNet  MATH  Google Scholar 

  • L.E. Baum, T. Petrie, G. Soules, N. Weiss, A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Stat. 41, 164–171 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  • L.E. Baum, G.R. Sell, Growth transformations for functions on manifolds. Pac. J. Math. 27(2), 211–227 (1968)

    Article  MathSciNet  MATH  Google Scholar 

  • R. Bellman, Dynamic Programming (Princeton University Press, New Jersey, 1957)

    MATH  Google Scholar 

  • R. Bertolami, H, Bunke, Ensemble methods for handwritten text line recognition systems, in IEEE International Conference on Systems, Man and Cybernetics, ed. by H. Bunke, pp. 2334–2339 (2005)

    Google Scholar 

  • U. Bhattacharya, B.B. Chaudhuri, Databases for research on recognition of handwritten characters of Indian scripts, in Proceedings of the 8th International Conference on Document Analysis and Recognition, Seoul, pp. 789–793 (2005)

    Google Scholar 

  • F. Bortolozzi, Jr S. Aires, L.S. Oliveira, M. Morita, Recent Advances in Handwriting Recognition, ed. by U. Pal, S.K. Parui, B.B. Chaudhuri. Document Analysis (Allied Publishers Pvt. Ltd, Kolkata, 2005), pp. 1–30

    Google Scholar 

  • H. Bunke, Recognition of cursive roman handwriting—past, present and future, in ICDAR (2003)

    Google Scholar 

  • R. Casey, G. Nagy, Recognition of printed Chinese characters. IEEE Trans. Electron. Comput. EC-15(1), 91–101 (1966)

    Google Scholar 

  • R.G. Casey, Moment normalization of handprinted characters. IBM J. Res. Dev. 14(5), 548–557 (1970). doi:10.1147/rd.145.0548

    Article  MathSciNet  MATH  Google Scholar 

  • S. Chang, D. Lo, An experimental system for the recognition of handwritten Chinese characters, in Proceedings of the 1st International Symposium on Computers and Chinese Input/Output Systems, pp. 257–267 (1973)

    Google Scholar 

  • B. Chen, B. Zhu, M. Nakagawa, Effects of generating a large amount of artificial patterns for on-line handwritten Japanese character recognition, in ICDAR pp. 663–667 (2011)

    Google Scholar 

  • X. Chen, T.-H. Su, T.-W. Zhang, Discriminative training of MQDF classifier on synthetic Chinese string samples, in Chinese Conference on Patten Recognition (CCPR) (2010)

    Google Scholar 

  • Y. Chen, X. Ding, Y. Wu, Off-line handwritten Chinese character recognition based on crossing line feature, in Proceedings of the 4th International Conference on Document Analysis and Recognition, Ulm, pp. 206–210 (1997)

    Google Scholar 

  • C.-C. Cheng, Online Learning of Large Margin Hidden Markov Models for Automatic Speech Recognition. Ph.D. thesis, University of California, San Diego, 2011

    Google Scholar 

  • C.C. Cheng, F. Sha, L.K. Saul, Online learning and acoustic feature adaptation in large-margin hidden Markov models. IEEE J. Sel. Top. Sign. Process. 4(6), 926–942 (2010)

    Article  Google Scholar 

  • C.K. Chow, An optimum character recognition system using decision functions. IRE Trans. Electron. Comput. EC-6(4), 247–254 (1957)

    Google Scholar 

  • D.C. Ciresan, U. Meier, J. Masci, L.M. Gambardella, J. Schmidhuber, Flexible, high performance convolutional neural networks for image classification, in Proceedings of the 22nd International Joint Conference on Artificial Intelligence (2011)

    Google Scholar 

  • R. Dai, C. Liu, B. Xiao, Chinese character recognition: history, status and prospects. Front. Comput. Sci. China 1(2), 126–136 (2007)

    Article  Google Scholar 

  • T.-M.-T. Do, Regularized bundle methods for large-scale learning problems with an application to large margin training of hidden Markov models. Ph.D. thesis, LIP6, Paris, 2010

    Google Scholar 

  • T.-M.-T. Do, T. Artières, Large margin training for hidden Markov models with partially observed states, in International Conference on Machine Learning (2009)

    Google Scholar 

  • R.O. Duda, P.E. Hart, Experiments in the recognition of hand-printed text, part II-context analysis, in Proceedings of the Fall Joint Computer Conference, pp. 1139–1139 (1968)

    Google Scholar 

  • R. Ehrich, K. Koehler, Experiments in the contextual recognition of cursive script. IEEE Trans. Comput. C-24(2), 182–194 (1975)

    Google Scholar 

  • R. Farag, Word-level recognition of cursive script. IEEE Trans. Comput. C-28(2), 172–175 (1979)

    Google Scholar 

  • B. Feng, X. Ding, Y. Wu, Chinese handwriting recognition using Hidden Markov Models, in Proceedings of the 16th International Conference on Pattern Recognition, Quebec, pp. 212–215 (2002)

    Google Scholar 

  • J.H. Friedman, Regularized discriminant analysis. J. Am. Stat. Assoc. 84(405), 165–175 (1989)

    Article  Google Scholar 

  • H. Fujisawa, Y. Nakano, K. Kurino, Segmentation methods for character recognition: from segmentation to document structure analysis. Proc. IEEE 80(7), 1079–1092 (1992). doi:10.1109/5.156471

    Article  Google Scholar 

  • K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd edn. (Academic Press, San Diego, 1990)

    MATH  Google Scholar 

  • J. Gao, X. Ding, Y. Wu, A segmentation algorithm for handwritten Chinese character strings, in Proceedings of the 5th International Conference on Document Analysis and Recognition, pp. 633–636 (1999)

    Google Scholar 

  • Y. Ge, Q. Huo, A comparative study of several modeling approaches for large vocabulary offline recognition of handwritten Chinese characters, in International Conference on Pattern Recognition, pp. 85–88 (IEEE Computer Society, Quebec, 2002)

    Google Scholar 

  • Y. Ge, Q. Huo, Z.D. Feng, Offline recognition of handwritten Chinese characters using Gabor features, CDHMM modeling and MCE training, in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Orlando, pp. 1053–1056 (2002)

    Google Scholar 

  • General Administration of Technology of the People’s Republic of China, Code of Chinese Graphic Character Set for Information Interchange–Primary Set (Standard Press of China, Beijing, 1980). (in Chinese)

    Google Scholar 

  • A. Graves, Supervised Sequence Labelling with Recurrent Neural Networks (Springer, Heidelberg, 2012)

    Book  MATH  Google Scholar 

  • A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, J. Schmidhuber, A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)

    Article  Google Scholar 

  • E.C. Greanias, Some important factors in the practical utilization of optical character readers ed. by G.L. Fischer. Optical Character Recognition (McGregor and Wemer, Washington, 1962), pp. 129–146

    Google Scholar 

  • S. Gunter, H. Bunke, Off-line cursive handwriting recognition using multiple classifier systems—on the influence of vocabulary, ensemble, and training set size. Opt. Lasers Eng. 43(3–5), 437–454 (2005)

    Article  Google Scholar 

  • N. Hagita, S. Naito, I. Masuda, Handprinted Kanji characters recognition based on pattem matching method, in Proceedings of the International Conference on Text Processing with a Large Character Set, pp. 169–114 (1983)

    Google Scholar 

  • M. Hamanaka, K. Yamada, J. Tsukumo, Normalization-cooperated feature extraction method for handprinted Kanji character recognition, in Proceedings of the 3rd International Workshop on Frontiers of Handwriting Recognition, pp. 343–348 (1993)

    Google Scholar 

  • H. Hao, X. Xiao, R. Dai, Handwritten Chinese character recognition by metasynthetic approach. Pattern Recogn. 30(8), 1321–1328 (1997)

    Article  Google Scholar 

  • T. He, Q. Huo, A study of a new misclassification measure for minimum classification error training of prototype-based pattern classifiers, in IEEE International Conference on Pattern Recognition, pp. 1–4 (2008)

    Google Scholar 

  • W. Highleyman, An analog method for character recognition. IRE Trans. Electron. Comput. EC-10, 502–512 (1961)

    Google Scholar 

  • J. Hull, A database for handwritten text recognition research. IEEE Trans. Pattern Anal. Mach. Intell. 16(5), 550–554 (1994)

    Article  Google Scholar 

  • I. Iijima, Y. Okumura, K. Kuwabara, New process of character recognition using sieving method. Inf. Control Res. 1(1), 30–35 (1963)

    Google Scholar 

  • T. Iijima, H. Genchi, K. Mori, A theoretical study of pattern identification by matching method, in Proceedings of the 1st USA-JAPAN Computer Conference, Tokyo, pp. 42–48 (1972)

    Google Scholar 

  • F. Jelinek, L.R. Bahl, R.L. Mercer, Design of a linguistic statistical decoder for recognition of continuous speech. IEEE Trans. Inf. Theory IT-21(3), 250–256 (1975)

    Google Scholar 

  • B.-S. Jeng, M.-W. Chang, S.-W. Sun, C.-H. Shih, T.-M. Wu, Optical Chinese character recognition with a hidden Markov model classifier—a novel approach. Electron. Lett. 26(18), 1530–1531 (1990)

    Article  Google Scholar 

  • Y. Jiang, X. Ding, Z. Ren, Substring alignment method for lexicon based handwritten Chinese string recognition and its application to address line recognition, in Proceedings of the 18th International Conference on Pattern Recognition, pp. 683–686 (2006)

    Google Scholar 

  • L. Jin, Y. Gao, G. Liu, Y. Li, K. Ding, SCUT-COUCH2009–a comprehensive online unconstrained Chinese handwriting database and benchmark evaluation. Int. J. Doc. Anal. Recogn. 14(1), 53–64 (2011)

    Article  Google Scholar 

  • L. Jin, G. Wei, Handwritten Chinese character recognition with directional decomposition cellular features. J. Circuit Syst. Comput. 8(4), 517–524 (1999)

    Article  Google Scholar 

  • B.H. Juang, W. Hou, C.H. Lee, Minimum classification error rate methods for speech recognition. IEEE Trans. Speech Audio Process. 5(3), 257–265 (1992)

    Article  Google Scholar 

  • E. Kavallieratou, N. Liolios, E. Koutsogeorgos, N. Fakotakis, G. Kokkinakis, The GRUHD database of Greek unconstrained handwriting, in Proceedings of the 6th International Conference on Document Analysis and Recognition, Seattle, pp. 561–565 (2001)

    Google Scholar 

  • A. Kawamura, K. Yura, T. Hayama, On-line recognition of freely handwritten Japanese characters using directional feature densities, in Proceedings of 11th ICPR, pp. 183–186 (1992)

    Google Scholar 

  • J. Keshet, C.-C. Cheng, M. Stoehr, D. McAllester, L.K. Saul, Direct error rate minimization of hidden Markov models, in IEEE International Conference on Acoustics, Speech and Signal Processing (2011)

    Google Scholar 

  • D.H. Kim, Y.S. Hwang, S.T. Park, E.J. Kim, S.H. Paek, S.Y. Bang, Handwritten Korean character image database PE92, in Proceedings of the 2nd International Conference on Document Analysis and Recognition, Tsukuba, pp. 470–473 (1993)

    Google Scholar 

  • D.H. Kim, Y.S. Hwang, S.T. Park, E.J. Kim, S.H. Paek, S.Y. Bang, Handwritten Korean character image database PE92. IEICE Trans. Inf. Syst. E79-D(7), 943–950 (1996)

    Google Scholar 

  • F. Kimura, K. Takashina, S. Tsuruoka, Y. Miyake, Modified quadratic discriminant functions and the application to Chinese character recognition. IEEE Trans. Pattern Anal. Mach. Intell. 9(1), 149–153 (1987)

    Article  Google Scholar 

  • T. Kohonen, The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)

    Article  Google Scholar 

  • Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE 86(1), 2278–2324 (1998)

    Article  Google Scholar 

  • S. Lee, Z. Chang, W. Wu, A. Huang, B. Ye, Radical extraction methods for processing and recognition of Chinese characters, in International Symposium on Computer. Architecture and Digital Signal Processing, pp. 480–483 (1989)

    Google Scholar 

  • K.-C. Leung, C.-H. Leung, Recognition of handwritten Chinese characters by combining regularization, fisher’s discriminant and distorted sample generation, in 10th ICDAR, pp. 1026–1030 (2009)

    Google Scholar 

  • G.-H. Li, P.-F. Shi, An approach to offline handwritten Chinese character recognition based on segment evaluation of adaptive duration. J. Zhejiang Univ. Sci. 5(11), 1392–1397 (2004)

    Article  Google Scholar 

  • N.-X. Li, L.-W. Jin, A Bayesian-based method of unconstrained handwritten offline Chinese text line recognition. Int. J. Doc. Anal. Recogn. (IJDAR) (2012). doi:10.1007/s10032-011-0178-0

  • N. Li, Writer-independent unconstrained handwritten offline Chinese text line recognition. Ph.D. thesis, South China University of Technology, Guangzhou 2010 (in Chinese)

    Google Scholar 

  • N. Li, X. Gao, L. Jin, Curved segmentation path generation for unconstrained handwritten Chinese text lines, in IEEE Asia Pacific Conference on Circuits and Systems, pp. 501–505 (2008)

    Google Scholar 

  • Y. Li, X. Ding, C.L. Tan, Combining character-based bigrams with word-based bigrams in contextual postprocessing for Chinese script recognition. ACM Trans. Asian Lang. Inf. Process. 1(4), 297–309 (2002)

    Article  Google Scholar 

  • Y. Li, C.L. Tan, An empirical study of statistical language models for contextual post-processing of Chinese script recognition, in Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition, pp. 257–262 (2004a)

    Google Scholar 

  • Y. Li, C.L. Tan, Influence of language models and candidate set size on contextual post-processing for Chinese script recognition, in Proceedings of the 17th International Conference on Pattern Recognition, pp. 537–540 (2004b)

    Google Scholar 

  • Y. Li, C.L. Tan, X. Ding, A hybrid post-processing system for offline handwritten Chinese script recognition. Pattern Anal. Appl. 8(3), 272–286 (2005)

    Article  MathSciNet  Google Scholar 

  • Y. Li, C.L. Tan, X. Ding, C. Liu, Contextual post-processing based on the confusion matrix in offline handwritten Chinese script recognition. Pattern Recogn. 37(9), 1901–1912 (2004)

    Article  MATH  Google Scholar 

  • Z. Liang, P. Shi, A metasynthetic approach for segmenting handwritten Chinese character strings. Pattern Recogn. Lett. 26(10), 1498–1511 (2005)

    Article  Google Scholar 

  • X. Lin, X. Ding, M. Chen, R. Zhang, Y. Wu, Adaptive confidence transform based classifier combination for Chinese character recognition. Pattern Recogn. Lett. 19(10), 975–988 (1998)

    Article  Google Scholar 

  • C.-L. Liu, Normalization-cooperated gradient feature extraction for handwritten character recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1465–1469 (2007)

    Article  Google Scholar 

  • C.-L. Liu, Handwritten Chinese Character Recognition: Effects of Shape Normalization and Feature Extraction, ed. by S. Jaeger, D. Doermann. Arabic and Chinese Handwriting Recognition, LNCS (Springer, Berlin, 2008), pp. 104–128

    Google Scholar 

  • C.-L. Liu, M. Koga, H. Fujisawa, Lexicon-driven segmentation and recognition of handwritten character strings for Japanese address reading. IEEE Trans. Pattern Anal. Mach. Intell. 24(11), 1425–1437 (2002)

    Article  Google Scholar 

  • C.-L. Liu, K. Marukawa, Global shape normalization for handwritten Chinese character recognition: a new method, in Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition, pp. 300–305 (2004)

    Google Scholar 

  • C.-L. Liu, K. Marukawa, Pseudo two-dimensional shape normalization methods for handwritten Chinese character recognition. Pattern Recogn. 38(12), 2242–2255 (2005)

    Article  Google Scholar 

  • C.-L. Liu, K. Nakashima, H. Sako, H. Fujisawa, Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recogn. 36(10), 2271–2285 (2003a). doi:10.1016/s0031-3203(03)00085-2

    Article  MATH  Google Scholar 

  • C.-L. Liu, H. Sako, H. Fujisawa, Handwritten Chinese character recognition: alternatives to nonlinear normalization, in Proceedings of the 7th International Conference on Document Analysis and Recognition, Edinburgh, Scotland, pp. 524–528 (2003b)

    Google Scholar 

  • C.-L. Liu, H. Sako, H. Fujisawa, Discriminative learning quadratic discriminant function for handwriting recognition. IEEE Trans. Neural Netw. 15(2), 430–444 (2004)

    Article  Google Scholar 

  • C.-L. Liu, F. Yin, D.-H. Wang, Q.-F. Wang, Chinese handwriting recognition contest 2010, in Chinese Conference on Patten Recognition (CCPR) (2010)

    Google Scholar 

  • C.-L. Liu, F. Yin, D.-H. Wang, Q.-F. Wang, CASIA online and offline Chinese handwriting databases. in ICDAR 37–41 (2011a)

    Google Scholar 

  • C.-L. Liu, F. Yin, Q.-F. Wang, D.-H. Wang, Chinese handwriting recognition competition, in International Conference on Document Analysis and Recognition (2011b)

    Google Scholar 

  • J.-H. Liu, Real time Chinese handwriting recognition. EE thesis, Massachusetts Institute of Technology, Cambridge, 1966

    Google Scholar 

  • Y.J. Liu, J.W. Tai, J. Liu, An introduction to the 4 million handwriting Chinese character samples library, in Proceedings of the International Conference on Chinese Computing and Orient Language Processing, Changsha, pp. 94–97 (1989)

    Google Scholar 

  • T. Long, L. Jin, Building compact MQDF classifier for large character set recognition by subspace distribution sharing. Pattern Recogn. 41(9), 2916–2925 (2008)

    Article  MATH  Google Scholar 

  • J.S. MacDonald, Experimental studies of handwriting signals. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, 1964

    Google Scholar 

  • K. Maeda, Y. Kurosawa, H. Asada, K. Sakai, S. Watanabe, Handprinted Kanji recognition by pattern matching method, in International Conference on Pattern Recognition, pp. 789–792 (1982)

    Google Scholar 

  • U.V. Marti, H. Bunke, A full English sentence database for off-line handwriting recognition, in Proceedings of the 5th International Conference on Document Analysis and Recognition, Bangalore, pp. 705–708 (1999)

    Google Scholar 

  • U.V. Marti, H. Bunke, Handwritten sentence recognition, in Proceedings of the 15th International Conference on Pattern Recognition, pp. 463–466 (2000)

    Google Scholar 

  • U.V. Marti, H. Bunke, The IAM-database: an English sentence database for off-line handwriting recognition. Int. J. Doc. Anal. Recogn. 5(1), 39–46 (2002)

    Article  MATH  Google Scholar 

  • K. Matsumoto, T. Fukushima, M. Nakagawa, Collection and analysis of on-line handwritten Japanese character patterns, in Proceedings of the 6th ICDAR, pp. 496–500 (2001)

    Google Scholar 

  • R. McDonald, K. Hall, G. Mann, Distributed training strategies for the structured perceptron, in North American Association for Computational Linguistics (NAACL), pp. 456–464 (2010)

    Google Scholar 

  • K. Mikolajczyk, C. Schmid, A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  • M. Mohamed, P. Gader, Handwritten word recognition using segmentation-free hidden Markov modeling and segmentation-based dynamic programming techniques. IEEE Trans. Pattern Anal. Mach. Intell. 18(5), 548–554 (1996)

    Article  Google Scholar 

  • S. Mori, K. Yamamoto, H. Yamada, T. Saito, On a handprinted Kyoiku-Kanji character data base. Bull. Electrotech. Lab. 43(11–12), 752–773 (1979)

    Google Scholar 

  • Y. Mori, K. Yokosawa, Neural networks that learn to discriminate similar Kanji characters. in Adv. Neural Inf. Process. Syst. 1, 332–339 (1989)

    Google Scholar 

  • J.H. Munson, Experiments in the recognition of hand-printed text, part l-character recognition, in Proceedings of the Fall Joint Computer Conference, San Francisco, pp. 1125–1125 (1968)

    Google Scholar 

  • S. Naito, N. Hagita, I. Masuda, Handprinted Kanji recognition by feature matching methods and its application to personal OCR, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 297–302 (1983)

    Google Scholar 

  • M. Nakagawa, T. Higashiyama, Y. Yamanaka, S. Sawada, L. Higashigawa, K. Akiyama, On-line handwritten character pattern database sampled in a sequence of sentences without any writing instructions, in Proceedings of the Fourth International Conference on Document Analysis and Recognition, Ulm, pp. 376–381 (1997)

    Google Scholar 

  • Y. Nakano, K. Nakata, Y. Uchikura, A. Nakajima, Improvement of Chinese character recognition using projection profiles, in International Conference on Pattern Recognition, pp. 172–178 (1973)

    Google Scholar 

  • R. Nopsuwanchai, Discriminative training methods and their applications to handwriting recognition. Ph.D. thesis, University of Cambridge, Cambridge, 2005

    Google Scholar 

  • N. Otsu, A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. SMC-9(1), 62–66 (1979)

    Google Scholar 

  • J.S. Park, H.J. Kang, S.W. Lee, Automatic quality measurement of gray-scale handwriting based on extended average entropy, in Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, pp. 426–429 (2000)

    Google Scholar 

  • T. Ploetz, G.A. Fink, Markov models for offline handwriting recognition: a survey. Int. J. Doc. Anal. Recogn. 12(4), 269–298 (2009)

    Article  Google Scholar 

  • W.S. Rohland, Character sensing system. USA Patent 2877951, Mar 1959 (1956)

    Google Scholar 

  • F. Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386–408 (1958). doi:10.1037/h0042519

    Article  MathSciNet  Google Scholar 

  • R. Rosenfeld, Two decades of statistical language modeling: where do we go from here? Proc. IEEE 88(8), 1270–1278 (2000)

    Article  Google Scholar 

  • T. Saito, H. Yamada, K. Yamamoto, On the data base ETL9 of handprinted characters in JIS Chinese characters and its analysis. IEICE Trans. J68-D(4), 757–764 (1985)

    Google Scholar 

  • A. Sato, K. Yamada, Generalized learning vector quantization. in Adv. Neural Inf. Process. Syst. 7, 423–429 (1995)

    Google Scholar 

  • K. Sayre, Machine recognition of handwritten words: a project report. Pattern Recogn. 5(3), 213–228 (1973)

    Article  Google Scholar 

  • A.W. Senior, A.J. Robinson, An off-line cursive handwriting recognition system. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 309–321 (1998)

    Article  Google Scholar 

  • F. Sha, Large margin training of acoustic models for speech recognition. Ph.D. thesis, University of Pennsylvania, Pennsylvania, 2007

    Google Scholar 

  • F. Sha, L.K. Saul, Comparison of large margin training to other discriminative methods for phonetic recognition by hidden Markov models, in IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, HI, pp. 313–316 (2007a)

    Google Scholar 

  • F. Sha, L.K. Saul, Large margin hidden Markov models for automatic speech recognition. Adv. Neural Inf. Process. Syst. 19, 1249–1256 (2007b)

    Google Scholar 

  • G. Srikantan, S.W. Lam, S.N. Srihari, Gradient-based contour encoding for character recognition. Pattern Recogn. 29(7), 1147–1160 (1996). doi:10.1016/0031-3203(95)00146-8

    Article  Google Scholar 

  • W. Stallings, Computer analysis of printed Chinese characters. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, 1971

    Google Scholar 

  • T.-H. Su, Off-line recognition of Chinese handwriting: from isolated character to realistic text. Ph.D. theses, Harbin Institute of Technology, Harbin, 2008 (in Chinese)

    Google Scholar 

  • T.-H. Su, C.-L. Liu, Improving HMM-based Chinese handwriting recognition using delta features and synthesized string samples, in International Conference on Frontiers in Handwriting Recognition (2010)

    Google Scholar 

  • T.-H. Su, C.-L. Liu, X.-Y. Zhang, Perceptron learning of modified quadratic discriminant function. in ICDAR (2011)

    Google Scholar 

  • T.-H. Su, T.-W. Zhang, D.-J. Guan, HIT-MW dataset for offline Chinese handwritten text recognition, in Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition, La Baule, INRIA–CCSd–CNRS, pp. 574–578 (2006)

    Google Scholar 

  • T.-H. Su, T.-W. Zhang, D.-J. Guan, Corpus-based HIT-MW database for offline recognition of general-purpose Chinese handwritten text. Int. J. Doc. Anal. Recogn. 10(1), 27–38 (2007a)

    Article  Google Scholar 

  • T.-H. Su, T.-W. Zhang, D.-J. Guan, Off-line recognition of realistic Chinese handwriting using segmentation-free strategy. Pattern Recogn. 42(1), 167–182 (2009)

    Article  MATH  Google Scholar 

  • T.-H. Su, T.-W. Zhang, H.-J. Huang, C.-L. Liu, Segmentation-free recognizer based on enhanced four plane feature for realistic Chinese handwriting, in IEEE Conference on Pattern Recognition (ICPR) (2008)

    Google Scholar 

  • T.-H. Su, T.-W. Zhang, H.-J. Huang, Y. Zhou, HMM-based recognizer with segmentation-free strategy for unconstrained Chinese handwritten text, in IEEE International Conference on Document Analysis and Recognition, pp. 133–137 (2007b)

    Google Scholar 

  • C.Y. Suen, M. Berthod, S. Mori, Automatic recognition of handprinted characters—the state of the art. Proc. IEEE 68(4), 469–487 (1980)

    Article  Google Scholar 

  • C.Y. Suen, C. Nadal, R. Legault, T.A. Mai, L. Lam, Computer recognition of unconstrained handwritten numerals. Proc. IEEE 80(7), 1162–1180 (1992)

    Article  Google Scholar 

  • Y.Y. Tang, C.Y. Suen, Document structures: a survey, in Proceedings of the Second International Conference on Document Analysis and Recognition, Tsukuba Science City, pp. 99–102 (1993)

    Google Scholar 

  • G. Tauschek, Reading machine. US Patent 2026329, Dec 1935 (1929)

    Google Scholar 

  • L.-T. Tu, Y.S. Lin, C.P. Yeh, I.S. Shyu, J.L. Wang, K.H. Joe, W.W. Lin, Recognition of handprinted Chinese characters by feature matching, in Proceedings of the 1st National Workshop on Character Recognition, pp. 166–175 (1991)

    Google Scholar 

  • C. Viard-Gaudin, P.M. Lallican, S. Knerr, P. Binter, The IRESTE On/Off (IRONOFF) dual handwriting database, in Proceedings of the 5th International Conference on Document Analysis and Recognition, Bangalore, pp. 455–458 (1999)

    Google Scholar 

  • A. Vinciarelli, S. Bengio, H. Bunke, Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 709–720 (2004)

    Article  Google Scholar 

  • T. Wakabayashi, S. Tsuruoka, F. Kimura, Y. Miyake, On the size and variable trans-formation of feature vector for handwritten character recognition. Trans. IEICE Jpn. J76-D-II(12), 2495–2503 (1993)

    Google Scholar 

  • P. Wang, R. Shiau, Machine recognition of printed Chinese characters via transformation algorithms. Pattern Recogn. 5(4), 303–321 (1973)

    Article  Google Scholar 

  • Q.-F. Wang, E. Cambria, C.-L. Liu, A. Hussain, Common sense knowledge for handwritten Chinese text recognition. Cogn. Comput. (2012a). doi:10.1007/s12559-012-9183-y

    Google Scholar 

  • Q.-F. Wang, F. Yin, C.-L. Liu, Integrating language model in handwritten Chinese text recognition, in International Conference on Document Analysis and Recognition, pp. 1036–1040 (2009)

    Google Scholar 

  • Q.-F. Wang, F. Yin, C.-L. Liu, Handwritten Chinese text recognition by integrating multiple contexts. IEEE Trans. Pattern Anal. Mach. Intell. 1469–1481 (2012b). doi:10.1109/TPAMI.2011.264

  • Q.R. Wang, C.Y. Suen, Analysis and design of a decision tree based on entropy reduction and its application to large character set recognition. IEEE Trans. Pattern Anal. Mach. Intell. 6(4), 406–417 (1984)

    Article  MATH  Google Scholar 

  • Y. Wang, X. Ding, C. Liu, MQDF discriminative learning based offline handwritten Chinese character recognition, in International Conference on Document Analysis and Recognition, pp. 1100–1104 (2011a)

    Google Scholar 

  • Y. Wang, X. Ding, C. Liu, MQDF retrained on selected sample set. IEICE Trans. 94-D(10), 1933–1936 (2011b)

    Google Scholar 

  • Y. Wang, Q. Huo, Sample-separation-margin based minimum classification error training of pattern classifiers with quadratic discriminant functions, in IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1866–1869 (2010)

    Google Scholar 

  • Y. Wang, Q. Huo, Building compact recognizers of handwritten Chinese characters using precision constrained Gaussian model, minimum classification error training and parameter compression. IJDAR 14(3), 255–262 (2011)

    Article  Google Scholar 

  • P.-K. Wong, C. Chan, Post-processing statistical language models for a handwritten Chinese character recognizer. IEEE Trans. Syst. Man Cybern. Part B 29(2), 286–291 (1999)

    Google Scholar 

  • Y. Wu, Teaching computer to recognize characters—brief introduction to Chinese character recogntion (Tsinghua University Press, Beijing, 2000). (in Chinese)

    Google Scholar 

  • L. Xu, A. Krzyzak, C.Y. Suen, Methods for combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 23(3), 418–435 (1992)

    Article  Google Scholar 

  • L. Xu, F. Yin, Q.-F. Wang, C.-L. Liu, Touching character separation in chinese handwriting using visibility-based foreground analysis, in ICDAR, pp. 859–863 (2011)

    Google Scholar 

  • J. Xue, X. Ding, C. Liu, R. Zhang, W. Qian, Location and interpretation of destination addresses on handwritten Chinese envelopes. Pattern Recogn. Lett. 22(6), 639–656 (2001)

    Article  MATH  Google Scholar 

  • H. Yamada, Contour DP matching method and its application to handprinted Chinese character recognition, in Proceedings of 7th International Conference on Pattern Recognition, pp. 389–392 (1984)

    Google Scholar 

  • H. Yamada, T. Saito, S. Mori, An improvement of correlation method—shift similarity. J. IECE Jpn. J64-D, 970–976 (1981)

    Google Scholar 

  • K. Yamamoto, Recognition of handprinted Kanji characters by relaxation matching. J. IECE Jpn. J65-D, 1167–1174 (1982)

    Google Scholar 

  • S. Yamamoto, A. Nakajima, K. Nakata, Chinese character recognition by hierarchical pattern matching, in International Conference on Pattern Recognition, pp. 187–196 (1973)

    Google Scholar 

  • Y. Yao, Handprinted Chinese character recognition via neural networks. Pattern Recogn. Lett. 7(1), 19–25 (1988)

    Article  Google Scholar 

  • M. Yasuda, H. Fujisawa, An improved correlation method for character recognition. Syst. Comput. Controls 10(2), 29–38 (1979)

    Google Scholar 

  • M. Yasuda, K. Yamamoto, H. Yamada, T. Saito, An improved correlation method for character recognition in a reciprocal feature field. IECE Trans. J68-D(3), 353–360 (1985)

    Google Scholar 

  • F. Yin, Q.-F. Wang, C.-L. Liu, Integrating geometric context for text alignment of handwritten Chinese documents, in ICFHR, pp. 7–12 (2010)

    Google Scholar 

  • M. Yoshida, M. Eden, Handwritten Chinese character recognition by an analysis-by-synthesis method, in International Conference on Pattern Recognition, pp. 197–204 (1973)

    Google Scholar 

  • H. Zhang, J. Guo, Introduction to HCL2000 database, in Proceedings of Sino-Japan Symposium on Intelligent Information Networks, Beijing (2000)

    Google Scholar 

  • S. Zhao, Z. Chi, P. Shi, H. Yan, Two-stage segmentation of unconstrained handwritten Chinese characters. Pattern Recogn. 36(1), 145–156 (2003)

    Article  MATH  Google Scholar 

  • S. Zhou, Q. Chen, X. Wang, HIT-OR3C: an opening recognition corpus for Chinese characters, in Proceedings of the 9th IAPR International Workshop on Document Analysis Systems (2010)

    Google Scholar 

  • X.-D. Zhou, C.-L. Liu, M. Nakagawa, Online handwritten Japanese character string recognition using conditional random fields, in ICDAR, pp. 521–525 (2009)

    Google Scholar 

  • X.-D. Zhou, J.-L. Yu, C.-L. Liu, T. Nagasaki, K. Marukawa, Online handwritten Japanese character string recognition incorporating geometric context, in ICDAR, pp. 48–52 (2007)

    Google Scholar 

  • G.K. Zipf, Human Behavior and the Principle of Least Effort (Addison-Wesley, Cambridge, 1949)

    Google Scholar 

  • M.J. Zobrak, A method for rapid recognition hand drawn line patterns. Ms thesis, University of Pittsburgh, Pennsylvania, 1966

    Google Scholar 

  • Y. Zou, K. Yu, K. Wang, Continuous Chinese handwriting recognition with language model, in 11th International Conference on Frontiers in Handwriting Recognition, pp. 344–348 (2008)

    Google Scholar 

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    Tonghua Su

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Su, T. (2013). Introduction. In: Chinese Handwriting Recognition: An Algorithmic Perspective. SpringerBriefs in Electrical and Computer Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31812-2_1

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