Autoadaptive neural networks applied to industrial characters recognition

Stephane Lecoeuche; Denis Deguillemont; Jean-Paul Dubus

doi:10.1117/12.258271

14 November 1996 Autoadaptive neural networks applied to industrial characters recognition

Stephane Lecoeuche, Denis Deguillemont, Jean-Paul Dubus

Proceedings Volume 2847, Applications of Digital Image Processing XIX; (1996) https://doi.org/10.1117/12.258271
Event: SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, 1996, Denver, CO, United States

Abstract

The character recognition in the industrial world is a complex problem because of the variety of supports and/or different types of writing. Nowadays, the employed systems are chosen in accordance of each case. To solve this problem, we present a system using two neural networks. The first one, a semi-supervised network, determines the required characteristics to the character recognition. It can extract an invariant mode whatever the position, the size and the orientation of the character are. The output of this first network is connected to the second one, a supervised network that executes a task of classification. Because of previous learning, this one recognizes the character. According to the quality of the recognition, the system may return to the first network the information required to improve the vision of character. This qualitative signal allow to modify the extracting process of the model. The interest of this system is this cooperation between two types of networks: the first one to carry out the vision, the second one to realize the multifont recognition, and the cooperation between these two networks allows to recognize the character whatever the support and the marking are.

Citation Download Citation

Stephane Lecoeuche, Denis Deguillemont, and Jean-Paul Dubus "Autoadaptive neural networks applied to industrial characters recognition", Proc. SPIE 2847, Applications of Digital Image Processing XIX, (14 November 1996); https://doi.org/10.1117/12.258271

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