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This paper presents the combination of Multi-Layer Perceptron (MLP) artificial neural network classifier for recognition printed Arabic digits. Different types of feature are used, cavity, zoning, pixel feature and Hu moment invariants which are invariant under change of size, translation, and orientation (rotation). On experimentation with a database of 6240 samples in multi-font and multi-size. We propose an approach based on a combination of Multi-Layer Perceptron using features on different origins. The technique yields an average recognition rate of 94.33% evaluated after three-methods of learning Kfold cross validation holdout and resubstitution method. It is useful for applications related to post office (postal address, postal sorting), car plates, barcode and can also be extended to Recognize handwritten Arabic Digit

Item Type	Conference
Divisions	» Symposium on Complex Systems and Intelligent Computing (CompSIC)
ePrint ID	417
Date Deposited	2015-09-19
Further Information	Google Scholar
URI	https://univ-soukahras.dz/en/publication/article/417

@inproceedings{uniusa417,
    title={Printed digits recognition using multiple multi Layer perceptron classifiers and Hu moments},
    author={Khedidja Derdour and Mouss Hayet},
    year={2015},
    booktitle={Symposium on Complex Systems and Intelligent Computing (CompSIC)}
}