@article {10.3844/ajeassp.2012.132.135, article_type = {journal}, title = {Recognition of Hindi (Arabic) Handwritten Numerals}, author = {Zaghloul, Rawan I. and Enas, Dojanah M.K. Bader and AlRawashdeh, F.}, volume = {5}, number = {2}, year = {2012}, month = {Aug}, pages = {132-135}, doi = {10.3844/ajeassp.2012.132.135}, url = {https://thescipub.com/abstract/ajeassp.2012.132.135}, abstract = {Recognition of handwritten numerals has been one of the most challenging topics in image processing. This is due to its contributions in the automation process in several applications. The aim of this study was to build a classifier that can easily recognize offline handwritten Arabic numerals to support those applications that are deal with Hindi (Arabic) numerals. A new algorithm for Hindi (Arabic) Numeral Recognition is proposed. The proposed algorithm was developed using MATLAB and tested with a large sample of handwritten numeral datasets for different writers in different ages. Pattern recognition techniques are used to identify Hindi (Arabic) handwritten numerals. After testing, high recognition rates were achieved, their ranges from 95% for some numerals and up to 99% for others. The proposed algorithm used a powerful set of features which proved to be effective in the recognition of Hindi (Arabic) numerals.}, journal = {American Journal of Engineering and Applied Sciences}, publisher = {Science Publications} }