Abstract
In this paper we present a comparison between two supervised classifiers, the first one is a statistic which is the K-Nearest Neighbors (KNN) while the second is a neuronal which is the multi-layer perceptron MLP in the recognition of cursive handwritten Arabic numerals. The recognition process is organized as follows: in the pre-processing of numeral images, we exploited the median filter, the thresholding, the centering and the normalization techniques, in the features extraction we have used the morphology mathematical method. The classification methods include the KNN and the MLP. The simulation results that we obtained demonstrate the MLP is more efficient than the KNN in this recognition. General Terms The K-Nearest Neighbors (KNN), The multi-layer perceptron (MLP), The mathematical morphology method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.