A New Feature Optimization Method Based on Two-Directional 2DLDA for Handwritten Chinese Character Recognition

LDA transformation is one of the popular feature dimension reduction techniques for the feature extraction in most handwritten Chinese characters recognition systems. The integration of the feature extraction and LDA transformation can be viewed as a two-directional feature transformation procedure, one is the pixel-level feature transformation by the summing up or blurring, another is by the LDA matrix, and the transformation coefficients are set empirically in the former. In this paper, we proposed a feature optimization method based on the gradient feature extraction by using the two-directional 2DLDA, which can find the optimal transformation coefficients in two directions. A series of experiments on the randomly selected 15 groups of the similar Chinese character samples from HCL2000 have indicated that, our method can effectively improve the recognition performance, the error rate reduction reaches 45.02% comparing to the traditional method, showing the effectiveness of the proposed approach.

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

[2]  C. R. Rao,et al.  The Utilization of Multiple Measurements in Problems of Biological Classification , 1948 .

[3]  Ming Li,et al.  2D-LDA: A statistical linear discriminant analysis for image matrix , 2005, Pattern Recognit. Lett..

[4]  Xiaoqing Ding,et al.  Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[5]  Cheng-Lin Liu,et al.  Classification and Learning for Character Recognition: Comparison of Methods and Remaining Problems , 2005 .

[6]  Fei Yin,et al.  Chinese Handwriting Recognition Contest 2010 , 2010, 2010 Chinese Conference on Pattern Recognition (CCPR).

[7]  Lianwen Jin,et al.  Handwritten Chinese Character Recognition with Directional Decomposition Cellular Features , 1998, J. Circuits Syst. Comput..

[8]  Jieping Ye,et al.  Two-Dimensional Linear Discriminant Analysis , 2004, NIPS.

[9]  Cheng-Lin Liu,et al.  Classification and Learning Methods for Character Recognition: Advances and Remaining Problems , 2008, Machine Learning in Document Analysis and Recognition.

[10]  Cheng-Lin Liu,et al.  Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[12]  Jian Yang,et al.  Two-dimensional discriminant transform for face recognition , 2005, Pattern Recognit..

[13]  Palaiahnakote Shivakumara,et al.  (2D)2LDA: An efficient approach for face recognition , 2006, Pattern Recognit..

[14]  Zhen-Long Bai,et al.  A study on the use of 8-directional features for online handwritten Chinese character recognition , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[15]  Qiang Huo,et al.  High performance Chinese OCR based on Gabor features, discriminative feature extraction and model training , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[16]  Hiroshi Sako,et al.  Handwritten digit recognition: investigation of normalization and feature extraction techniques , 2004, Pattern Recognit..