A Novel Technique for English Font Recognition Using Support Vector Machines

Font Recognition is one of the Challenging tasks in Optical Character Recognition. Most of the existing methods for font recognition make use of local typographical features and connected component analysis. In this paper, English font recognition is done based on global texture analysis. The main objective of this proposal is to employ support vector machines (SVM) in identifying various fonts. The feature vectors are extracted by making use of Gabor filters and the proposed SVM is trained using these features. The method is found to give superior performance over neural networks by avoiding local minima points. The SVM model is formulated tested and the results are presented in this paper. It is observed that this method is content independent and the SVM classifier shows an average accuracy of 93.54%.

[1]  Yingming Hao,et al.  Integral Image Based Fast Algorithm for Two-Dimensional Otsu Thresholding , 2008, 2008 Congress on Image and Signal Processing.

[2]  Hubert Emptoz,et al.  Type extraction and character prototyping using gabor filters , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[3]  Fang Yang,et al.  An improved font recognition method based on texture analysis , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[4]  Mandana Hamidi,et al.  Support Vector Machine for Persian Font Recognition , 2007 .

[5]  Chi-Fang Lin,et al.  Chinese text distinction and font identification by recognizing most frequently used characters , 2001, Image Vis. Comput..

[6]  Sargur N. Srihari,et al.  Multifont classification using typographical attributes , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[7]  Li Chen,et al.  Character Independent Font Recognition on a Single Chinese Character , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Tieniu Tan,et al.  Font Recognition Based on Global Texture Analysis , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  K. P. Soman,et al.  Machine Learning with SVM and other Kernel methods , 2009 .

[10]  Chong Zhang,et al.  A Chinese document layout analysis method based on minimal spanning tree clustering , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[11]  Rolf Ingold,et al.  Optical Font Recognition Using Typographical Features , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Bidyut Baran Chaudhuri,et al.  Automatic detection of italic, bold and all-capital words in document images , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[13]  Jonathan J. Hull,et al.  Font and Function Word Identification in Document Recognition , 1996, Comput. Vis. Image Underst..