A class-modular feedforward neural network for handwriting recognition

Since the conventional feedforward neural networks for character recognitio n have been deigned to classify a large number of classes with one large ne twork structure, inevitably it poses the very complex problem of determinin g the optimal decision boundaries for all the classes involved in a high-di mensional feature space. Limitations also exist in several aspects of the t raining and recognition processes. This paper introduces the class modulari ty concept to the feedforward neural network classifier to overcome such li mitations. In the class-modular concept, the original K-classification prob lem is decomposed into K 2-classification subproblems. A modular architectu re is adopted which consists of K subnetworks. each responsible for discrim inating a class from the other K-1 classes. The primary purpose of this pap er is to prove the effectiveness of class-modular neural networks in terms of their convergence and recognition power. Several cases have been studied , including the recognition of handwritten numerals (10 classes), English c apital letters (26 classes). touching numeral pairs (100 classes), and Kore an characters in postal addresses (352 classes). The test results confirmed the superiority of the class-modular neural network and the interesting as pects on further investigations of the class modularity paradigm. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.