HYBRID SCHEMES OF HOMOGENEOUS AND HETEROGENEOUS CLASSIFIERS FOR CURSIVE WORD RECOGNITION

Jin Ho KIM1,2, Kye Kyung KIM1 and Ching Y. SUEN1

1CENPARMI,Concordia University, Montreal, Quebec, Canada
E­mail: {kjinho, kkkim, suen}@cenparmi.concordia.ca

2 Department of Electronic Engineering, Kyungil University,Kyungsan, Kyungpook, Korea
E­mail: kjinho@bear.kyungil.ac.kr

Sophisticated hybrid schemes of the homogeneous and heterogeneous classifiers for cursive word recognition are presented. Two homogeneous MLPs (multi­layer perceptrons) are combined into a new single powerful classifier at the architectural level, and HMM (hidden Markov model) is added to the new classifier as a heterogeneous one at the output level. This is based on the idea that classifiers with more different methodologies and different features can better complement each other. The presented scheme achieves a recognition rate of 92.7% for English legal words of a CENPARMI database, a performance which is better than several previous hybrid schemes reported in the literature.

In: L.R.B. Schomaker and L.G. Vuurpijl (Eds.)
Proceedings of the Seventh International Workshop on Frontiers
in Handwriting Recognition, September 11-13 2000, Amsterdam,
Nijmegen: International Unipen Foundation,
ISBN 90-76942-01-3
pp. 433-442.