Model generation and cooperation in on-line omni-writer handwriting recognition

In this paper, we introduce a new method for on-line character recognition based on the cooperation of two classifiers. The first one is a k-nearest-neighbor classifier, the second one is an evolutionary neural classifier. Several cooperation architectures (already tested in OCR but seldom in on-line recognition) are presented, from the easier (weighted sum of both classifier outputs) to the most complicated (integrating neural network). The recognition improvement varies between 30% and 40% according to the merging strategy. We try to appreciate each methods assets on recognition rate and speed. Results are presented on 52 different character classes (upper and lower case letters) and more than 50000 examples from UNIPEN database.