Handwritten character recognition on focused on the segmentation of character prototypes in small strips

Handwritten character recognition on focused on the segmentation of character prototypes in small strips

Автор: Ali Benafia, Smaine Mazouzi, Benafia Sara

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 12 vol.9, 2017 года.

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The automatic recognition of handwriting is a particularly complex operation. Until now, there is no algorithm able to recognize handwriting without that; there are assumptions to take in advance to facilitate the task of the process. A handwritten text can contain letters lowercase, uppercase letters, characters sticks and digits. Therefore, it is capital to know extract and separate all these different units in order to process them with specific algorithms to their class handwriting. In this paper, we present a system for unconstrained handwritten text recognition, which allows to achieve this operation thanks to an intelligent segmentation based on an iterative cutting in a multi-script environment. The results obtained from the experimental protocol reach an "equal error rate" (EER) neighboring to 6%. These calculations were calculated with a relatively small base; however this same rate can be decreased with great bases. Our results are extremely encouraging for the simple reason that our approach is situated in a more general context unlike other approaches which define several non-rigid assumptions; this clearly makes the problem simpler and may make it trivial.

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Character recognition, feature extraction, off-line handwriting analysis, segmentation

Короткий адрес: https://sciup.org/15016442

IDR: 15016442   |   DOI: 10.5815/ijisa.2017.12.04

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