Accumulated-recognition-rate normalization for combining multiple on/off-line Japanese character classifiers tested on a large database

Authors:
Ondrej Velek;Stefan Jaeger;Masaki Nakagawa
Affiliations:
Graduate School of Technology, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan;Graduate School of Technology, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan;Graduate School of Technology, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan
Venue:
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Year:
2003

Citing 10
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Pattern Recognition

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Abstract

This paper presents a technique for normalizing likelihood of multiple classifiers, allowing their fair combination. Our technique generates for each recognizer one general or several stroke-number specific characteristic functions. A simple warping process maps output scores into an ideal characteristic. A novelty of our approach is in using a characteristic based on the accumulated recognition rate, which makes our method very robust and stable to random errors in training data and requires no smoothing prior to normalization. In this paper we test our method on a large database named Kuchibue_d, a publicly available benchmark for on-line Japanese handwritten character recognition and very often used for benchmarking new methods.