Local blur correction for document images

Image quality is a hot topic since most of image analysis and recognition systems are sensitive to degradation. In this paper, we are interested in the quality of document images to recover text content with as few errors as possible. One of the defects that degrades the Optical Character Recognition rate (OCR) is blur. Therefore, we propose to detect blur and to attenuate its effect on an OCR result. As blur is non-uniform on the document area, we propose a local approach. No prior model is chosen for blur that may be of various natures. Thanks to a local clustering based on a novel blur feature, we build a debluring adapted to the heterogeneous blur. As a result, the blurred image is locally corrected according to the blur type. This method focuses on the ease of character segmentation, then makes OCR more efficient. The experiments are carried out on a public database DIQA together with the use of an OCR. The obtained results show that the proposed method gives an overall improvement of 11% in OCR rate.