Share Email Print

Proceedings Paper

Comparative evaluation of different classifiers for robust distorted-character recognition
Author(s): Basil As-Sadhan; Ziad Al Bawab; Ammar El Seed; Mohammed Noamany
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 16 January 2006
PDF: 8 pages
Proc. SPIE 6067, Document Recognition and Retrieval XIII, 60670O (16 January 2006); doi: 10.1117/12.643156
Show Author Affiliations
Basil As-Sadhan, Carnegie Mellon Univ. (United States)
Ziad Al Bawab, Carnegie Mellon Univ. (United States)
Ammar El Seed, Carnegie Mellon Univ. (United States)
Mohammed Noamany, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 6067:
Document Recognition and Retrieval XIII
Kazem Taghva; Xiaofan Lin, Editor(s)

© SPIE. Terms of Use
Back to Top