Predicting Mobile-Captured Document Images Sharpness Quality

Nowadays the number of mobile applications is fast growing. Among them, mobile applications based on Optical Character Recognition (OCR) play an important role. One of the main challenge of such applications to overcome is that the image acquisition procedure is in a manner unreliable and may contain many distortions. As a consequence, a suitable OCR output requires efforts to enhance the quality of the captured image. This leads to an increase of computation time and cost. In this perspective, we focus on the prediction of image's sharpness quality. We choose to concentrate on image's sharpness quality because blur distortions seriously alter readability for both human and computer. Our contribution consists of a method combining focus and sharpness measures with a Support Vector Machine to classify image's sharpness quality. This approach is fast, reliable, and can be easily implemented on mobile devices. Experimental results show that our method is efficient for OCR based mobile-captured document images.

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