Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
The matter of handwritten text recognition is as yet a major challenge to mainstream researchers. A few ways deal with this challenge have been endeavored in the most recent years, for the most part concentrating on the English pre-printed or handwritten characters space. Consequently, the need to effort a research concerning to Arabic texts handwritten recognition. The Arabic handwriting presents unique technical difficulties because it is cursive, right to left in writing and the letters convert its shapes and structures when it is putted at initial, middle, isolation or at the end of words. In this study, the Arabic text recognition is developed and designed to recognize image of Arabic text/characters. The proposed model gets a single line of Arabic text, which convert and segments into words and then segments into letters. A multilayer feed forward neural network is trained to recognize these segments as characters. The final results indicate and clarify that the proposed system perform an effective accuracy of recognition rated up to 83% for Arabic text.
How to cite this article:
Ali Mohsin and Mohammed Sadoon. Developing an Arabic Handwritten Recognition System by
Means of Artificial Neural Network.
DOI: https://doi.org/10.36478/jeasci.2020.1.3
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2020.1.3