Abstract

This paper aims to present a data set (images) of Tifinagh handwritten Corona virus COVID-19 glossary and a comprehensive approach that allows this glossary recognition. This approach is based on Radon transform in all steps of this process. In pre-processing phase, this transform intervenes for skew detection and correction. Regarding text segmentation, Radon transform allows transforming text lines into positioned peaks in order to easily extract all lines. Finally, this transform allows recognizing handwritten Tifinagh characters with a very high recognition rate.

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