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Proceedings Paper

Sequential neural network combination for degraded machine-printed character recognition
Author(s): Abderrahmane Namane; Madjid Arezki; Abderrezak Guessoum; El Houssine Soubari; Patrick P. Meyrueis; Michel M. Bruynooghe
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Paper Abstract

This paper presents an OCR method that combines Hopfield network with two layer perceptron for degraded printed character recognition. Hopfield network stores 35 prototype characters used as main classes. After the pre-processing, an image of a character is given to Hopfield network which can yield after a fixed iteration number, a pattern that is subsquently fed to MLP for classification. The main idea is to enhance or restore such degraded character images with Hopfield model at different iteration number for recognition accuracy applied to poor quality bank check. We report experimental results for a comparison of three neural architectures: the Hopfield network, the MLP-based classifier and the proposed combined architecture. Classification accuracy for ten digits and twenty five alphabetic characters from a single font is also studied in the presence of additive Gaussian noise. The paper reports 100% recognition rate at different levels of noise. Experimental results show an achievement of 99.35% of recognition rate on poor quality bank check characters, which confirm that the proposed approach can be successfully used for effective degraded printed character recognition.

Paper Details

Date Published: 17 January 2005
PDF: 10 pages
Proc. SPIE 5676, Document Recognition and Retrieval XII, (17 January 2005); doi: 10.1117/12.587162
Show Author Affiliations
Abderrahmane Namane, Univ. Louis Pasteur (France)
Univ. de Blida (Algeria)
Madjid Arezki, Univ. de Blida (Algeria)
Abderrezak Guessoum, Univ. de Blida (Algeria)
El Houssine Soubari, Univ. Louis Pasteur (France)
Patrick P. Meyrueis, Univ. Louis Pasteur (France)
Michel M. Bruynooghe, Univ. Louis Pasteur (France)

Published in SPIE Proceedings Vol. 5676:
Document Recognition and Retrieval XII
Elisa H. Barney Smith; Kazem Taghva, Editor(s)

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