An off-line oriental character recognition system (OOCRS): synergy of distortion modeling, hidden Markov models and vector quantization

Off-line handwritten oriental character recognition is a difficult task due to the large category and stroke variety. These oriental characters are made up of components known as radicals, which are often written in a distorted proportion and size. All these factors lead to a difficult recognition problem, which unfortunately cannot be solved using direct classification approach like the neural network classifier and a preprocessing module. This paper proposes several novel preprocessing approaches and synergy of classifiers to achieve good performance. Novel classification approaches, comprising rough and coarse classification modules are proposed which when combined appropriately produced a high-performance recognition system capable of producing high accuracy classification in off-line oriental character recognition. The recognition accuracy of the system is a high of 97% and a 99% for the top 5 candidate selection scores.

[1]  Nei Kato,et al.  A Handwritten Character Recognition System Using Directional Element Feature and Asymmetric Mahalanobis Distance , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Khue Hiang. Chan.  Handwriting recognition using high performance computing platforms. , 1997 .

[3]  V. K. Govindan,et al.  Character recognition - A review , 1990, Pattern Recognit..

[4]  Ching Y. Suen,et al.  The Combination of Multiple Classifiers by A Neural Network Approach , 1995, Int. J. Pattern Recognit. Artif. Intell..

[5]  Wentai Liu,et al.  Optical recognition of handwritten Chinese characters: Advances since 1980 , 1993, Pattern Recognit..

[6]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[7]  Sevki S. Erdogan,et al.  Incremental learning for linear fusion of handwritten Chinese character classifiers , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[8]  L. R. Rabiner,et al.  On the application of vector quantization and hidden Markov models to speaker-independent, isolated word recognition , 1983, The Bell System Technical Journal.

[9]  Ching Y. Suen,et al.  Robust stroke segmentation method for handwritten Chinese character recognition , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[10]  Seong-Whan Lee,et al.  Adaptive nonlinear shape matching for unconstrained handwritten character recognition , 1995, Pattern Recognit..

[11]  Seong-Whan Lee,et al.  Nonlinear shape normalization methods for the recognition of large-set handwritten characters , 1994, Pattern Recognit..

[12]  Fumitaka Kimura,et al.  Handwritten numerical recognition based on multiple algorithms , 1991, Pattern Recognit..

[13]  Kuo-Chin Fan,et al.  A recursive hierarchical scheme for radical extraction of handwritten Chinese characters , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[14]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[15]  Yoshiki Mizukami.  A handwritten Chinese character recognition system using hierarchical displacement extraction based on directional features , 1998, Pattern Recognit. Lett..

[16]  Rui Zhang,et al.  Adaptive confidence transform based classifier combination for Chinese character recognition , 1998, Pattern Recognit. Lett..

[17]  R. Bordley.  A Multiplicative Formula for Aggregating Probability Assessments , 1982 .

[18]  Seong-Whan Lee,et al.  A truly 2-D hidden Markov model for off-line handwritten character recognition , 1998, Pattern Recognit..

[19]  Makoto Kobayashi,et al.  Off-line character recognition using HMM by multiple directional feature extraction and voting with bagging algorithm , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[20]  Korris Fu-Lai Chung,et al.  Offline handwritten Chinese character recognition via radical extraction and recognition , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[21]  Qiang Huo,et al.  Contextual vector quantization modeling of hand-printed Chinese character recognition , 1995, Proceedings., International Conference on Image Processing.

[22]  Philip A. Chou,et al.  Document Image Decoding Using Markov Source Models , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Hsin-Chia Fu,et al.  Recognition of handwritten similar Chinese characters by neural networks , 1996, Neural Networks for Signal Processing VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop.

[24]  William B. Levy,et al.  Maximum entropy aggregation of individual opinions , 1994, IEEE Trans. Syst. Man Cybern..

[25]  Kazuhiko Yamamoto,et al.  Research on Machine Recognition of Handprinted Characters , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.