A Study on the Combination of Classifiers for Handwritten Digit Recognition

• Authors: Sven Behnke, Marcus Pfister, and Raul Rojas

 
• In Proceedings of Neural Networks in Applications, Third International Workshop (NN'98) - Magdeburg, Germany, pp. 39-46, 1998.

 
• Abstract:
This article presents a case study on the combination of classifiers for the recognition of handwritten digits. Four different classifiers are briefly described and evaluated using the NIST-digits data set. Different parallel and sequential combination schemes are introduced. Furthermore, it is described how to tune the sequential combination using a boosting technique. These combination methods are benchmarked using the NIST-digits. The experimental results indicate that all investigated classifier combinations outperform the best individual classifier. The sequential combination yields slightly better results than the parallel combination and has a much lower computational complexity. In addition, it is possible to improve the performance of the sequential combination by boosting.
 
• Full paper: nn98.pdf