A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
A System towards Indian Postal Automation
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Similarity-based training set acquisition for continuous handwriting recognition
Information Sciences: an International Journal
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This paper presents a multi-classifier system using classifiers based on two different approaches. A stochastic model using Markov Random Field is combined with different kind of neural networks by several fusing rules. It has been proved that the combination of different classifiers can lead to improve the global recognition rate. We propose to compare different fusing rules in a framework composed of classifiers with high accuracies. We show that even there still remains a complementarity between classifiers, even from the same approach, that improves the global recognition rate. The combinations have been tested on handwritten digits. The overall recognition rate has reached 99.03% without using any rejection criteria.