A multi-layer perceptron approach to threshold documents with complex background. Rabelo, J., Zanchettin, C., Mello, C., & Bezerra, B. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2011. doi abstract bibtex This paper describes a thresholding method based on a Multi-Layer Perceptron approach for documents with complex backgrounds. The study case is focused on two regions of Brazilian bank checks: the courtesy amount and the character magnetic code. Those images have complex backgrounds with different patterns, which is a problem for an automatic recognition system. The new approach is based on a connectionistic approach to find the best threshold value. The proposed method is compared to ten thresholding algorithms (classical and specific for bank checks) in three different real bank checks databases, according to different evaluation metrics (recognition rate, peak signal-to-noise ratio, mean square error, precision, recall, accuracy, specificity, negative rate metric, misclassification penalty metric and f-measure). Based on the results, we may conclude the proposed method is more robust to variations in the image acquiring process, which influences the contrast, bright, hue and amount of noise verified in the image. © 2011 IEEE.
@inproceedings{
title = {A multi-layer perceptron approach to threshold documents with complex background},
type = {inproceedings},
year = {2011},
keywords = {Document Image Processing,Image Processing,Neural Networks},
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last_modified = {2019-02-14T18:02:00.888Z},
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abstract = {This paper describes a thresholding method based on a Multi-Layer Perceptron approach for documents with complex backgrounds. The study case is focused on two regions of Brazilian bank checks: the courtesy amount and the character magnetic code. Those images have complex backgrounds with different patterns, which is a problem for an automatic recognition system. The new approach is based on a connectionistic approach to find the best threshold value. The proposed method is compared to ten thresholding algorithms (classical and specific for bank checks) in three different real bank checks databases, according to different evaluation metrics (recognition rate, peak signal-to-noise ratio, mean square error, precision, recall, accuracy, specificity, negative rate metric, misclassification penalty metric and f-measure). Based on the results, we may conclude the proposed method is more robust to variations in the image acquiring process, which influences the contrast, bright, hue and amount of noise verified in the image. © 2011 IEEE.},
bibtype = {inproceedings},
author = {Rabelo, J.C.B. and Zanchettin, C. and Mello, C.A.B. and Bezerra, B.L.D.},
doi = {10.1109/ICSMC.2011.6084056},
booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}
}
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