Benefit of multiclassifier systems for Arabic handwritten words recognition

In order to improve the results of single classifiers, the study of multiple classifier systems has become an area of intensive research in pattern recognition. In this paper, two types of features are fed to a number of artificial neural networks (ANN). Then, their respective responses are combined for the recognition of handwritten Arabic literal words. Different parallel combination schemes are presented, including the use of an ANN as a meta classifier. Their results are then compared and conclusions on the most suitable approach are drawn.

[1]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Berkman Sahiner,et al.  Dual system approach to computer-aided detection of breast masses on mammograms. , 2006, Medical physics.

[5]  Robert P. W. Duin,et al.  The combining classifier: to train or not to train? , 2002, Object recognition supported by user interaction for service robots.

[6]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[7]  Venu Govindaraju,et al.  The Role of Holistic Paradigms in Handwritten Word Recognition , 2009 .

[8]  Theo Pavlidis,et al.  Algorithms for Graphics and Imag , 1983 .

[9]  Brijesh Verma,et al.  Neural-based solutions for the segmentation and recognition of difficult handwritten words from a benchmark database , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[10]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[11]  Josef Kittler,et al.  A Framework for Classifier Fusion: Is It Still Needed? , 2000, SSPR/SPR.

[12]  Sargur N. Srihari,et al.  Off-Line Cursive Script Word Recognition , 1989, IEEE Trans. Pattern Anal. Mach. Intell..