In order to improve recognition results, decisions of several classifiers can be combined. The combination can be accomplished in different ways depending on the types of information produced by the individual classifiers. This chapter considers combination methods that can be applied when the information is provided at both the abstract and measurement levels. For abstract-level classifiers, the combination methods discussed in this chapter consist of majority vote, weighted majority vote with weights derived from a genetic search algorithm, Bayesian formulation, a Behavior-Knowledge Space method. To combine the decisions of measurement-level classifiers, a multi-layer perception is used. Theoretical considerations and experimental results on handwritten characters are presented, and the results show that combining multiple classifiers is an effective means of producing highly reliable decisions for both categories of classifiers. Copyright © 1997 World Scientific Publishing.
|Title of host publication||Handbook of character recognition and document image analysis|
|Editors||H. BUNKE , P. S. P. WANG|
|Place of Publication||Singapore|
|Publisher||World Scientific Publishing|
|Publication status||Published - 1997|
CitationLam, L., Huang, Y.-S., & Suen, C. Y. (1997). Combination of multiple classifier decisions for optical character recognition. In H. Bunke & P. S. P. Wang (Eds.), Handbook of character recognition and document image analysis (pp. 79-101). Singapore: World Scientific Publishing.
- Combination of classifiers
- Majority vote
- Genetic algorithm
- Neural networks