An efficient way of combining SVMs for handwritten digit recognition

Authors:
Renata F. P. Neves;Cleber Zanchettin;Alberto N. G. Lopes Filho
Affiliations:
Center of Informatics, Federal University of Pernambuco, Recife, Brazil;Center of Informatics, Federal University of Pernambuco, Recife, Brazil;Center of Informatics, Federal University of Pernambuco, Recife, Brazil
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Year:
2012

Citing 12
Cited 0

Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Digital Image Processing

Digital Image Processing
Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
A novel cascade ensemble classifier system with a high recognition performance on handwritten digits

Pattern Recognition
A SVM-based cursive character recognizer

Pattern Recognition
SVM-based hierarchical architectures for handwritten Bangla character recognition

International Journal on Document Analysis and Recognition
Markov models for offline handwriting recognition: a survey

International Journal on Document Analysis and Recognition
Handwritten character recognition through two-stage foreground sub-sampling

Pattern Recognition
Deep, big, simple neural nets for handwritten digit recognition

Neural Computation
A robust free size OCR for omni-font persian/arabic printed document using combined MLP/SVM

CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
A comparison of methods for multiclass support vector machines

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a method of combining SVMs (support vector machines) for multiclass problems that ensures a high recognition rate and a short processing time when compared to other classifiers. This hierarchical SVM combination considers the high recognition rate and short processing time as evaluation criteria. The used case study was the handwritten digit recognition problem with promising results.