Comparative evaluation of different classifiers for robust distorted-character recognition

Basil As-Sadhan; Ziad Al Bawab; Ammar El Seed; Mohammed Noamany

doi:10.1117/12.643156

16 January 2006 Comparative evaluation of different classifiers for robust distorted-character recognition

Basil As-Sadhan, Ziad Al Bawab, Ammar El Seed, Mohammed Noamany

Proceedings Volume 6067, Document Recognition and Retrieval XIII; 60670O (2006) https://doi.org/10.1117/12.643156
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

This paper investigates and compares between applying the algorithms of Support Vector Machine (SVM), Principal Component Analysis (PCA), Individual Principal Component Analysis (iPCA), Linear Discriminant Analysis (LDA), and Single-Nearest-Neighbor Method (1-NNM) to distorted-character recognition. Applying SVM achieves a classification error rate of 2.15% on the Letter-Image Dataset [Frey and Slate 1991]. This error rate is statistically comparable to the best number in the literature on this dataset that the authors are aware of, which is 2%. This was archived by a fully connected MLP neural network with adaboosting, where training was performed on 20 machines [Schwenk and Bengio 1997]. However, using SVM on a single machine, takes less than 3.5 minutes for training. The features of the dataset and the errors committed by SVM were analyzed in an attempt to combine classifiers and reduce the error rate. We report the results achieved for the different techniques used.

Citation Download Citation

Basil As-Sadhan, Ziad Al Bawab, Ammar El Seed, and Mohammed Noamany "Comparative evaluation of different classifiers for robust distorted-character recognition", Proc. SPIE 6067, Document Recognition and Retrieval XIII, 60670O (16 January 2006); https://doi.org/10.1117/12.643156

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