Dr. Abderrahmane Namane Profile

Dr. Abderrahmane Namane

at Univ Louis Pasteur

SPIE Involvement:

Author

Publications (2)

SPIE Journal Paper | 1 August 2006

Hopfield-multilayer-perceptron serial combination for accurate degraded printed character recognition

Abderrahmane Namane, Abderrezak Guessoum, El Houssine Soubari, Mustapha Djebari, Patrick Meyrueis, Michel Bruynooghe

OE, Vol. 45, Issue 08, 087201, (August 2006) https://doi.org/10.1117/12.10.1117/1.2345056

KEYWORDS: Optical character recognition, Optical engineering, Image segmentation, Network architectures, Image quality, Neural networks, Image classification, Signal to noise ratio, Neurons, Quality measurement

Read Abstract +

Degraded printed character recognition is a hard and ever-present problem in optical character recognition. Previous work has explored serial combination of multilayer perceptron (MLP) and autoassociators networks for printed character recognition. The MLP is used as a first step classifier for its discrimination capability and is not very well suited for rejection. On the other hand, the autoassociator used as a second step classifier is more appropriate when a very small error and rejection are required. Unfortunately, this better behavior with respect to rejection is either paid in terms of rejection error or in terms of computational complexity, particularly when the number of classes is high. In this paper, we propose a serial combination of the Hopfield and MLP networks in order to achieve accurate recognition of degraded printed characters. We introduce a relative distance and use it as a quality measurement of the degraded character, which makes the Hopfield-based classifier very powerful and very well-suited for rejection. This relative distance is compared to a rejection threshold in order to accept or reject the incoming degraded character by the Hopfield model used as a first classifier. Due to its discrimination capability, the MLP network is used as a second classifier to avoid rejection error and to diminish computational complexity. The proposed method is devoted to solving the problem of recognition of single font characters collected from poor quality bank checks. We report experimental results from a comparison of three neural architectures: the Hopfield network, the MLP-based classifier, and the proposed combined architecture. The proposed method is also compared to five other recognition systems. It is shown that the proposed architecture exhibits the best performance, with no significant increase in the computational burden. In this paper, we propose also a bank check processing procedure for account check number (ACN) detection, localization, and character retrieval...

Proceedings Article | 17 January 2005 Paper

Sequential neural network combination for degraded machine-printed character recognition

Abderrahmane Namane, Madjid Arezki, Abderrezak Guessoum, El Houssine Soubari, Patrick Meyrueis, Michel Bruynooghe

Proceedings Volume 5676, (2005) https://doi.org/10.1117/12.587162

KEYWORDS: Optical character recognition, Neural networks, Network architectures, Electronic imaging, Prototyping, Image classification, Image quality, Image enhancement, Image restoration

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years