Classification of Printed Personalized English Isolated- Word-Error Using SVM Method

A better understanding on word classification could lead to a better detection and correction techniques. We used different features or attributes to represent a machine-printed English word, and support vector machines is used to evaluate those features into two class types of word. Our proposed model classifies the words by using fewer words during the training process because those training words are considered personalized words. Our results are very encouraging when compared with neural networks, Hamming distance or minimum edit distance technique; with further improvement in sight.

[1]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[2]  Dinggang Shen,et al.  Design efficient support vector machine for fast classification , 2005, Pattern Recognit..

[3]  Rashad Al-Jawfi,et al.  Handwriting Arabic character recognition LeNet using neural network , 2009, Int. Arab J. Inf. Technol..

[4]  Attaullah Khawaja,et al.  Classification of printed Chinese characters by using neural network , 2006 .

[5]  S.K. Tiong,et al.  Non-Technical Loss analysis for detection of electricity theft using support vector machines , 2008, 2008 IEEE 2nd International Power and Energy Conference.

[6]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[7]  N. Nicolosi,et al.  Feature Selection Methods for Text Classification , 2008 .

[8]  Sayan Mukherjee,et al.  Feature Selection for SVMs , 2000, NIPS.

[9]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Karen Kukich,et al.  Techniques for automatically correcting words in text , 1992, CSUR.

[12]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[13]  Richard F. Lyon,et al.  On-line hand-printing recognition with neural networks , 1996, Proceedings of Fifth International Conference on Microelectronics for Neural Networks.

[14]  Renu Dhir,et al.  Identification of Printed Punjabi Words and English Numerals Using Gabor Features , 2011 .

[15]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .