Design of novel post-processing algorithms for handwritten Arabic numerals classification Online publication date: Mon, 22-Jan-2018
by Mahabub Hasan Mahalat; Ayatullah Faruk Mollah; Subhadip Basu; Mita Nasipuri
International Journal of Applied Pattern Recognition (IJAPR), Vol. 4, No. 4, 2017
Abstract: The process of handwritten character recognition is more challenging in comparison with printed character recognition because of individual specific differences in writing style. Although, extensive research has been carried out, classification accuracy is yet to be improved for practical applications. In the current work, some novel post-processing techniques have been presented for improving recognition performance of isolated handwritten Arabic numerals using multi-class support vector machines. The system is trained and tested using a database of 3,000 handwritten Arabic numeral samples. A set of 60 features consisting of 24 shadow, 16 octant-centroid and 20 longest-run features is employed in the current work. Post-processing improves the classification accuracy from 98.10% to 99.30%. The samples that still remain misclassified are found to be heavily deformed. It may be stated that this work is suitable for practical applications.
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