Estimating the Effects of Text Genre, Image Resolution and Algorithmic Complexity needed for Sinhala Optical Character Recognition
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While optical character recognition for Latin based scripts have seen near human quality performance, the accuracy for the rounded scripts of South Asia still lag behind. Work on Sinhala OCR has mainly reported on performance on constrained classes of font faces and so been inconclusive. This paper provides a comprehensive series of experiments using conventional machine learning as well as deep learning on texts and font faces of diverse types and in diverse resolutions, in order to present a realistic estimation of the complexity of recognizing the rounded script of Sinhala. While texts of both old and contemporary books can be recognized with over 87% accuracy, those in old newspapers are much harder to recognize owing to poor print quality and resolution.
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