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DOI: 10.14569/IJACSA.2011.021006
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A Statistical Approach For Latin Handwritten Digit Recognition

Author 1: Ihab Zaqout

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 2 Issue 10, 2011.

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Abstract: A simple method based on some statistical measurements for Latin handwritten digit recognition is proposed in this paper. Firstly, a preprocess step is started with thresholding the gray-scale digit image into a binary image, and then noise removal, spurring and thinning are performed. Secondly, by reducing the search space, the region-of-interest (ROI) is cropped from the preprocessed image, then a freeman chain code template is applied and five feature sets are extracted from each digit image. Counting the number of termination points, their coordinates with relation to the center of the ROI, Euclidian distances, orientations in terms of angles, and other statistical properties such as minor-to-major axis length ratio, area and others. Finally, six categories are created based on the relation between number of termination points and possible digits. The present method is applied and tested on training set (60,000 images) and test set (10,000 images) of MNIST handwritten digit database. Our experiments report a correct classification of 92.9041% for the testing set and 95.0953% for the training set.

Keywords: Digit recognition; freeman chain coding; feature extraction; classification.

Ihab Zaqout, “A Statistical Approach For Latin Handwritten Digit Recognition ” International Journal of Advanced Computer Science and Applications(IJACSA), 2(10), 2011. http://dx.doi.org/10.14569/IJACSA.2011.021006

@article{Zaqout2011,
title = {A Statistical Approach For Latin Handwritten Digit Recognition },
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2011.021006},
url = {http://dx.doi.org/10.14569/IJACSA.2011.021006},
year = {2011},
publisher = {The Science and Information Organization},
volume = {2},
number = {10},
author = {Ihab Zaqout}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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