Evaluating a zoning mechanism and class-modular architecture for handwritten characters recognition

Authors:
Sandra De Avila;Leonardo Matos;Cinthia Freitas;João M. De Carvalho
Affiliations:
Federal University of Sergipe, Comp. Depart., São Cristóvão, SE, Brazil;Federal University of Sergipe, Comp. Depart., São Cristóvão, SE, Brazil;Pontifical Catholic University of Parana, Curitiba, PR, Brazil;Federal University of Campina Grande, Campina Grande, PB, Brazil
Venue:
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Year:
2007

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Abstract

In this article we propose a feature extraction procedure based on directional histograms and investigate the application of a nonconventional neural network architecture, applied to the problem of handwritten character recognition. This approach is inspired on some characteristics of the human visual system, as it focus attention on high spatial frequencies and on the recognition of local features. Two architectures were tested and evaluated: a conventional MLP (Multiple Layer Perceptron) and a class-modular MLP. Experiments developed with the Letter database produced a recognition rate of 93.67% for the class-modular MLP. Other set of experiments utilized the IRONOFF database resulting in recognition rates of 89.21% and 80.75% for uppercase and lowercase characters respectively, also with the class-modular MLP.