Abstract
This paper focuses on the possibilities of optimization of the training process of an MLP neural net using Backpropagation as a learning algorithm, employed as a classifier in an Optical Character Recognition (OCR) application. Also, the process for determination of a set of optimal parameters describing the characters that conform each class is described. The processing and analysis of the images in BMP, GIF, JPG and TIF format are included. A comparative study of the possibilities of improvement of the learning process of an MLP net employing heuristics for its design and training is made. As a fundamental result a substantial improvement of the net learning process is obtained and an OCR of great reliability is built.
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© 2002 Springer-Verlag Berlin Heidelberg
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Cruz, I.B., Díaz Sardiñas, A., Bello Pérez, R., Sardiñas Oliva, Y. (2002). Learning Optimization in a MLP Neural Network Applied to OCR. In: Coello Coello, C.A., de Albornoz, A., Sucar, L.E., Battistutti, O.C. (eds) MICAI 2002: Advances in Artificial Intelligence. MICAI 2002. Lecture Notes in Computer Science(), vol 2313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46016-0_31
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DOI: https://doi.org/10.1007/3-540-46016-0_31
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