[1] Huang Heming, Da Feipeng, Han Xiaoxu, et al. Wavelet transform and gradient direction based feature extractionmethod for off-line handwritten Tibetan letter recognition [J]. Journal of Southeast University (English Edition), 2014, 30 (1): 27-31. [doi:10.3969/j.issn.1003-7985.2014.01.006]

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Wavelet transform and gradient direction based feature extractionmethod for off-line handwritten Tibetan letter recognition()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

30

Issue:

2014 1

Page:

27-31

Research Field:

Computer Science and Engineering

Publishing date:

2014-03-31

Info

Title:

Wavelet transform and gradient direction based feature extractionmethod for off-line handwritten Tibetan letter recognition

Author(s):

Huang Heming¹;  2;  Da Feipeng¹;  Han Xiaoxu³

¹School of Automation, Southeast University, Nanjing 210096, China
²School of Computer Science, Qinghai Normal University, Xining 810008, China
³Department of Computer and Information Science, Fordham University, New York 10458, USA

Keywords:

pattern recognition;  wavelet transform;  gradient direction;  Tibetan;  handwritten character

PACS:

TP391.4

DOI:

10.3969/j.issn.1003-7985.2014.01.006

Abstract:

To improve the recognition accuracy of off-line handwritten Tibetan characters, the local gradient direction histograms based on the wavelet transform are proposed as the recognition features. First, for a Tibetan character sample image, the first level approximation component of the Haar wavelet transform is calculated. Secondly, the approximation component is partitioned into several equal-sized zones. Finally, the gradient direction histograms of each zone are calculated, and the local direction histograms of the approximation component are considered as the features of the character sample image. The proposed method is tested on the recently developed off-line Tibetan handwritten character sample database. The experimental results demonstrate the effectiveness and efficiency of the proposed feature extraction method. Furthermore, compared with the detail components, the approximation component contributes more to the recognition accuracy.

References:

[1] Huang H M, Da F P. General structure based collation of Tibetan syllables [J]. Journal of Computational Information System, 2010, 6(5):1693-1703.
[2] Wang H J, Zhao N Y, Deng G Y. A stroke segment extraction algorithm for Tibetan character recognition [J]. Journal of Chinese Information Processing, 2001, 15(4): 41-46.(in Chinese)
[3] Li Y Z, Wang Y L, Liu Z Z. Study on printed Tibetan character recognition technology [J]. Journal of Nanjing University: Natural Sciences Edition, 2012, 48(1): 55-62.(in Chinese)
[4] Ngodrup, Zhao D C. Research on wooden blocked Tibetan character segmentation based on drop penetration algorithm [C]//Chinese Conference on Pattern Recognition. Chongqing, China, 2010: 84-88.
[5] Liang B, Wang W L, Qian J J. Application of hidden Markov model in on-line recognition of handwritten Tibetan characters [J]. Journal of Microelectronics and Computer, 2009, 26(4): 98-101.
[6] Ma L L, Liu H D, Wu J. MRG-OHTC database for online handwritten Tibetan character recognition [C]//International Conference on Document Analysis and Recognition. Beijing, China, 2011: 207-211.
[7] Huang H M, Da F P. A database for off-line handwritten Tibetan character recognition [J]. Journal of Computational Information System, 2012, 9(18): 5987-5993.
[8] Huang H M, Da F P. Sparse representation-based classification algorithm for optical Tibetan character recognition [J]. Optik-International Journal for Light and Electron Optics, 2014, 125(3):1034-1037.
[9] Huang H M, Da F P. Wavelet and moments based off-line handwritten Tibetan character recognition[J]. Journal of Information and Computational Science, 2013, 10(6): 1855-1859.
[10] Raviraj P, Sanavallah M Y. The modified 2D-Haar wavelet transformation in image compression [J]. Middle-East Journal of Scientific Research, 2007, 2(2): 73-78.
[11] Liu C L. Normalization-cooperated gradient feature extraction for handwritten character recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(8):1465-1469.

Memo

Memo:

Biographies: Huang Heming(1969—), male, graduate; Da Feipeng(corresponding author), male, doctor, professor, dafp@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.60963016), the National Social Science Foundation of China(No.17BXW037).
Citation: Huang Heming, Da Feipeng, Han Xiaoxu. Wavelet transform and gradient direction based feature extraction method for off-line handwritten Tibetan letter recognition[J].Journal of Southeast University(English Edition), 2014, 30(1):27-31.[doi:10.3969/j.issn.1003-7985.2014.01.006]

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