Sequential neural network combination for degraded machine-printed character recognition
Abdelkader Namane, Madjid Arezki, Abderrezak Guessoum, El Houssine Soubari, Patrick P. Meyrueis, Michel M. Bruynooghe · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
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.