RECOGNITION OF HANDWRITTEN WORDED AMOUNTS ON CHECKS USING SYMBOLIC AND NEURAL CLASSIFIERS

摘要

This paper proposes symbolic and neural classifiers to read unconstrained handwritten worded amounts in bankchecks. Features are extracted from the binary image of the worded amount. Depending on the features extracted, some words are recognized entirely symbolically, some words entirely neurally, and the remaining both symbolically and neurally. Results of experiments at word level and check level are provided.