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Offline Chinese handwriting recognition: an assessment of current technology

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Abstract

Offline Chinese handwriting recognition (OCHR) is a typically difficult pattern recognition problem. Many authors have presented various approaches to recognizing its different aspects. We present a survey and an assessment of relevant papers appearing in recent publications of relevant conferences and journals, including those appearing in ICDAR, SDIUT, IWFHR, ICPR, PAMI, PR, PRL, SPIEDRR, and IJDAR. The methods are assessed in the sense that we document their technical approaches, strengths, and weaknesses, as well as the data sets on which they were reportedly tested and on which results were generated. We also identify a list of technology gaps with respect to Chinese handwriting recognition and identify technical approaches that show promise in these areas as well as identify the leading researchers for the applicable topics, discussing difficulties associated with any given approach.

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References

  1. Shi D M, Damper R I, Gunn S R. Offline handwritten Chinese character recognition by radical decomposition. ACM Transactions on Asian Language Information Processing. 2003, 1: 27–48

    Article  Google Scholar 

  2. Kim I J, Kim J H. Statistical character structure modeling and its application to handwritten Chinese character recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2003, 25: 1422–1436

    Article  Google Scholar 

  3. Lu Y, Shridhar M. Character segmentation in handwritten words-An overview. Pattern Recognition. 1996, 29: 77–96

    Article  Google Scholar 

  4. Wei X H, Ma S P, Jin Y J. Segmentation of connected Chinese characters based on genetic algorithm. In: ICDAR’05: Proceedings of the Ninth International Conference on Document Analysis and Recognition, Seoul, Korea, Vol 1. IEEE Computer Society, 2005, 645–649

    Google Scholar 

  5. Liang Z Z, Shi P F. A metasynthetic approach for segmenting handwritten Chinese character strings. Pattern Recognition Letters. 2005, 26: 1498–1511

    Article  Google Scholar 

  6. Zhao S Y, Chi Z R, Shi P F, et al. Two-stage segmentation of unconstrained handwritten Chinese characters. Pattern Recognition, 2003, 36: 145–156

    Article  MATH  Google Scholar 

  7. Zhao S Y, Chi Z R, Shi P F, et al. Handwritten Chinese character segmentation using a two-stage approach. In: Proceedings of the sixth International Conference on Document Analysis and Recognition (ICDAR’01),Seattle, WA, Vol 1. IEEE Computer Society, 2001, 179–183

    Article  Google Scholar 

  8. Tseng Y H, Lee H J. Recognition-based handwritten Chinese character segmentation using a probabilistic Viterbi algorithm. Pattern Recognition Letters. 1999, 20: 791–806

    Article  Google Scholar 

  9. Tseng L Y, Chen R C. Segmenting handwritten Chinese characters based on heuristic merging of stroke bounding boxes and dynamic programming. Pattern Recognition Letters. 1998, 19: 963–973

    Article  Google Scholar 

  10. Dong J X, Krzyzak A, Suen C Y. An improved handwritten Chinese character recognition system using support vector machine. Pattern Recognition Letters. 2005, 26: 1849–1856

    Article  Google Scholar 

  11. Liu C L, Marukawa K M. Pseudo two-dimensional shape normalization methods for handwritten Chinese character recognition. Pattern Recognition. 2005, 38: 2242–2255

    Article  Google Scholar 

  12. Tseng Y H, Lee H J. Interfered-character recognition by removing interfering-lines and adjusting feature weights. In: Proceedings of Fourteenth International Conference on Pattern Recognition. 1998, Vol 2. 1865–1867

    Article  Google Scholar 

  13. Chiu H P, Tseng D C. A feature-preserved thinning algorithm for handwritten Chinese characters. In: Proceedings of 13th International Conference on Pattern Recognition, 1996, Vol 3. 235–239

    Article  Google Scholar 

  14. Gao J, Ding X q, Wu Y S. A segmentation algorithm for handwritten Chinese character strings. In: Proceedings of the Fifth International Conference on Document Analysis and Recognition(ICDAR’99), India, Vol 1. IEEE Computer Society. 1999, 633–636

    Google Scholar 

  15. Wang Q, Chi Z R, Feng D D, et al. Match between normalization schemes and feature sets for handwritten Chinese character recognition. In: Proceedings of the Sixth International Conference on Document Analysis and Recognition(ICDAR’01), Seattle, Vol 1. IEEE Computer Society, 2001, 551–555

    Article  Google Scholar 

  16. Liu C L, Sako H, Fujisawa H. Handwritten Chinese character recognition: alternatives to nonlinear normalization. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition(ICDAR’03), Scotland, Vol 1. IEEE Computer Society, 2003, 524–528

    Article  Google Scholar 

  17. Liu C L, Marukawa K. Global shape normalization for handwritten Chinese character recognition: a new method. In: Ninth International Workshop on Frontiers in Handwriting Recognition, Tokyo, Japan, Vol 1. 2004, 300–305

    Article  Google Scholar 

  18. Liu C L. Handwriting Chinese character recognition: Effects of shape normalization and feature extraction. In: Summit on Arabic and Chinese Handwriting, College Park, USA. 2006, 13

  19. Shi D, Gunn S R, Damper R I. Handwritten Chinese radical recognition using nonlinear active shape models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25: 277–280

    Article  Google Scholar 

  20. Shi D M, Gunn S R, Damper R I. Handwritten Chinese character recognition using nonlinear active shape models and the Viterbi algorithm. Pattern Recognition Letters, 2002, 23: 1853–1862

    Article  MATH  Google Scholar 

  21. Ng G S, Shi D, Gunn S R, et al. Nonlinear active handwriting models and their applications to handwritten Chinese radical recognition. In: ICDAR’03: Proceedings of the Seventh International Conference on Document Analysis and Recognition, Edinburgh, Scotland, Vol 1. IEEE Computer Society, 2003, 534–538

    Article  Google Scholar 

  22. Shi D, Gunn S R, Damper R I. A radical approach to handwritten Chinese character recognition using active handwriting models. In: Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol 1. 2001, 670

    Google Scholar 

  23. Shi D, Gunn S R, Damper R I. Active radical modelling for handwritten Chinese characters. In: ICDAR’01: Proceedings of the sixth International Conference on Document Analysis and Recognition, Seattle, WA, Vol 1. IEEE Computer Society, 2001, 236–240

    Article  Google Scholar 

  24. Wang A B, Fan K C, Wu W H. A recursive hierarchical scheme for radical extraction of handwritten Chinese characters. In: Proceedings of 13th International Conference on Pattern Recognition, Vol 3. 1996, 240–244

    Article  Google Scholar 

  25. Lin F, Tang X O. Off-line handwritten Chinese character stroke extraction. In: Proceedings of 16th International Conference on Pattern Recognition, Vol 3. 2002, 249–252

    Google Scholar 

  26. Su Y M, Wang J F. Decomposing Chinese characters into stroke segments using SOGD filters and orientation normalization. In: Proceedings of the 17th International Conference on Pattern Recognition, Vol 2. 2004, 351–354

    Article  Google Scholar 

  27. Su Y M, Wang J F. A novel stroke extraction method for Chinese characters using Gabor filters. Pattern Recognition, 2003, 36: 635–647

    Article  Google Scholar 

  28. Cao R N, Tan C L. A model of stroke extraction from Chinese character images. In: Proceedings of 15th International Conference on Pattern Recognition, Vol 4. 2000, 368–371

    Google Scholar 

  29. Fan K C, Wu W H. A run-length coding based approach to stroke extraction of Chinese characters. In: Proceedings of 15th International Conference on Pattern Recognition, Vol 2. 2000, 565–568

    Google Scholar 

  30. Chiu H P, Tseng D C. A novel stroke-based feature extraction for handwritten Chinese character recognition. Pattern Recognition, 1999, 32: 1947–1959

    Article  Google Scholar 

  31. Kim J W, Kim K I, Choi B J, et al. Decomposition of Chinese character into strokes using mathematical morphology. Pattern Recognition Letters, 1999, 20: 285–292

    Article  MATH  Google Scholar 

  32. Zeng J, Liu Z Q. Markov random fields for handwritten Chinese character recognition. In: ICDAR’05: Proceedings of the Ninth International Conference on Document Analysis and Recognition, Korea, Vol 1. IEEE Computer Society, 2005, 101–105

    Google Scholar 

  33. Wang Q, Chi Z r, Feng D D, et al. Hidden Markov random field based approach for off-line handwritten Chinese character recognition. In: Proceedings of 15th International Conference on Pattern Recognition, Vol 2. 2000, 347–350

    Google Scholar 

  34. Chen Z, Lee Ch W, Cheng R H. Handwritten Chinese character analysis and preclassification using stroke structural sequence. In: Proceedings of 13th International Conference on Pattern Recognition, Vol 3. 1996, 89–93

    Article  Google Scholar 

  35. Kim I J, Liu C L, Kim J H. Stroke-guided pixel matching for handwritten Chinese character recognition. In: ICDAR’99: Proceedings of the Fifth International Conference on Document Analysis and Recognition,India, Vol 1. IEEE Computer Society, 1999, 665–668

    Google Scholar 

  36. Liu C L, Kim I J, Kim J H. Model-based stroke extraction and matching for handwritten Chinese character recognition. Pattern Recognition, 2001, 34: 2339–2352

    Article  MATH  Google Scholar 

  37. Ge Y, Huo Q. A comparative study of several modeling approaches for large vocabulary offline recognition of handwritten Chinese characters. In:Proceedings of 16th International Conference on Pattern Recognition, Vol 3. 2002, 85–88

    Google Scholar 

  38. Ge Y, Huo Q. A study on the use of CDHMM for large vocabulary off-line recognition of handwritten Chinese characters. In: Proceedings of Eighth International Workshop on Frontiers in Handwriting Recognition, Canada, Vol 1. 2002, 334–338

    Google Scholar 

  39. Ge Y, Huo Q, Feng Z D. Offline recognition of handwritten Chinese characters using Gabor features, CDHMM modeling and MCE training. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol 1. 2002, I-1053–I-1056

    Google Scholar 

  40. Kato N, Suzuki M, Omachi S, et al. A handwritten character recognition system using directional element feature and asymmetric Mahalanobis distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21: 258–262

    Article  Google Scholar 

  41. Liu H L, Ding X Q. Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes. In: Proceedings of the Ninth International Conference on Document Analysis and Recognition, Korea, Vol 1. IEEE Computer Society, 2005, 19–23

    Google Scholar 

  42. Feng B, Ding X Q, Wu Y S. Chinese handwriting recognition using hidden Markov models. In: Proceedings of 16th International Conference on Pattern Recognition, Vol 3. 2002, 212–215

    Google Scholar 

  43. Zhang R, Ding X Q. Minimum classification error training for handwritten character recognition. In: Proceedings of 16th International Conference on Pattern Recognition, Vol 1. 2002, 580–583

    Google Scholar 

  44. Wu M R, Zhang B, Zhang L. A neural network based classifier for handwritten Chinese character recognition. In: Proceedings of 15th International Conference on Pattern Recognition, Vol 2.2000, 561–564

    Google Scholar 

  45. Wang C H, Xiao B H, Dai R W. A new integration scheme with multilayer perceptron networks for handwritten Chinese character recognition. In: Proceedings of 15th International Conference on Pattern Recognition, Vol 2. 2000, 961–964

    Google Scholar 

  46. Tsang C K Y, Chung F L. Development of a structural deformable model for handwriting recognition. In: Proceedings of Fourteenth International Conference on Pattern Recognition, Vol 2. 1998, 1130–1133

    Article  Google Scholar 

  47. Tsang C K Y, Chung F L. A structural deformable model with application to post-recognition of handwriting. In: Proceedings of 15th International Conference on Pattern Recognition, Vol 2. 2000, 129–132

    Google Scholar 

  48. Xiao B H, Wang C H, Dai R W. Adaptive combination of classifiers and its application to handwritten Chinese character recognition. In: Proceedings of 15th International Conference on Pattern Recognition, Vol 2. 2000, 327–330

    Google Scholar 

  49. Zhang J Y, Ding X Q, Liu C S. Multi-scale feature extraction and nested-subset classifier design for high accuracy handwritten character recognition. In: Proceedings of 15th International Conference on Pattern Recognition, Vol 2. 2000, 581–584

    Google Scholar 

  50. Shioyama T, Wu H Y, Nojima T. Recognition algorithm based on wavelet transform for handprinted Chinese characters. In: Proceedings of Fourteenth International Conference on Pattern Recognition, Vol 1. 1998, 229–232

    Article  Google Scholar 

  51. Tseng D C, Chiu H P. Fuzzy ring data for invariant handwritten Chinese character recognition. In: Proceedings of 13th International Conference on Pattern Recognition, Vol 3. 1996, 94–98

    Article  Google Scholar 

  52. Shioyama T, Hamanaka J. Recognition algorithm for handprinted Chinese characters by 2D-FFT. In: Proceedings of 13th International Conference on Pattern Recognition, Vol 3. 1996, 225–229

    Article  Google Scholar 

  53. Mizukami Y. A handwritten Chinese character recognition system using hierarchical displacement extraction based on directional features. Pattern Recognition Letters, 1998, 19: 595–604

    Article  MATH  Google Scholar 

  54. Guo F J, Zhen L X, Ge Y, et al. An efficient candidate set size reduction method for coarse-classifier of Chinese handwriting recognition. In: Summit on Arabic and Chinese Handwriting, College Park, USA. 2006, 41–46

  55. Fu C. Techniques for solving the large-scale classification problem in Chinese handwriting recognition. In: Summit on Arabic and Chinese Handwriting, College Park, USA. 2006, 87–92

  56. Xiong Y, Huo Q, Chan C K. A discrete contextual stochastic model for the off-line recognition of handwritten Chinese characters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23: 774–782

    Article  Google Scholar 

  57. Wang C H, Hotta Y, Suwa M, et al. Handwritten Chinese address recognition. In: Proceedings of Ninth International Workshop on Frontiers in Handwriting Recognition, Tokyo, Japan, Vol 1. 2004, 539–544

    Article  Google Scholar 

  58. Han Z, Liu C P, Yin X C. A two-stage handwritten character segmentation approach in mail address recognition. In: Proceedings of the Ninth International Conference on Document Analysis and Recognition, Korea, Vol 1. IEEE Computer Society, 2005, 111–115

    Google Scholar 

  59. Fu Q, Ding X Q, Liu C S, et al. A hidden Markov model based segmentation and recognition algorithm for Chinese handwritten address character strings. In: Proceedings of the Ninth International Conference on Document Analysis and Recognition, Seoul, Korea, Vol 2. IEEE Computer Society, 2005, 590–594

    Google Scholar 

  60. Tang H S, Augustin E, Suen C Y, et al. Spiral recognition methodology and its application for recognition of Chinese bank checks. In: Proceedings of Ninth International Workshop on Frontiers in Handwriting Recognition, 2004, Tokyo, Japan, Vol 1. 2004, 263–268

    Article  Google Scholar 

  61. Lin X f, Ding X q, Chen M, et al. Adaptive confidence transform based classifier combination for Chinese character recognition. Pattern Recognition Letters, 1998, 19: 975–988

    Article  Google Scholar 

  62. Hung K Y, Luk R W P, Yeung D S, et al. A multiple classifier approach to detect Chinese character recognition errors. Pattern Recognition, 2005, 38: 723–738

    Article  Google Scholar 

  63. Wu T L, Ma S P. Feature extraction by hierarchical overlapped elastic meshing for handwritten Chinese character recognition. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, Edinburgh, Scotland, Vol 1. IEEE Computer Society, 2003, 529–533

    Article  Google Scholar 

  64. Ding X Q, Liu H L. Segmentation-driven offline handwritten Chinese and Arabic script recognition. In: Summit on Arabic and Chinese Handwriting, 2006, College Park, USA. 2006, 61–73

  65. Huang L, Huang X. Multiresolution recognition of offline handwritten Chinese characters with wavelet transform. In: Proceedings of the sixth International Conference on Document Analysis and Recognition, Seattle, WA, Vol 1. IEEE Computer Society, 2001, 631–634

    Article  Google Scholar 

  66. Wang A B, Fan K C. Optical recognition of handwritten Chinese characters by hierarchical radical matching method. Pattern Recognition, 2001, 34: 15–35

    Google Scholar 

  67. Li Y X, Tan C L. An empirical study of statistical language models for contextual post-processing of Chinese script recognition. In: Proceedings of Ninth International Workshop on Frontiers in Handwriting Recognition, Tokyo, Japan, Vol 1. 2004, 257–262

    MathSciNet  Google Scholar 

  68. Li Yuan-Xiang, Tan Chew Lim. 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, 2004, Vol 2. 2004, 537–540

    Article  Google Scholar 

  69. Natarajan P, Saleem S, Prasad R, et al. Multi-lingual offline handwriting recognition using Markov models. In: Summit on Arabic and Chinese Handwriting, 2006, College Park, USA. 2006, 177–187

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Authors and Affiliations

  1. Center of Excellence for Document Analysis and Recognition (CEDAR), Department of Computer Science and Engineering, University at Buffalo, State University of New York, Amherst, NY, 14228, USA

    Sargur N. Srihari, Xuanshen Yang & Gregory R. Ball

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  1. Sargur N. Srihari
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  2. Xuanshen Yang
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  3. Gregory R. Ball
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Srihari, S.N., Yang, X. & Ball, G.R. Offline Chinese handwriting recognition: an assessment of current technology. Front. Comput. Sc. China 1, 137–155 (2007). https://doi.org/10.1007/s11704-007-0015-2

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