Title

Document image quality assessment based on improved gradient magnitude similarity deviation

Document Type

Conference publication

Publication details

Alaei, A, Conte, D & Raveaux, R 2015, 'Document image quality assessment based on improved gradient magnitude similarity deviation', in Proceedings of the 13th International Conference on Document Analysis and Recognition (ICDAR 2015), Tunis, Tunisia, 23-26 August, IEEE, USA, pp. 176-180. ISBN: 9781479918065

Peer Reviewed

Peer-Reviewed

Abstract

Digitization of business processes and the use of mobile devices as portable scanner lead to a massive production of document images that is beyond manual handling. In such a scenario, automatic estimation of document image quality is a concern in order to adapt as early as possible document image analysis methods. In this paper, a method for full reference document image quality assessment (DIQA) using mainly foreground information is proposed. In the proposed method, a segmentation technique is employed on a reference document image to approximately separate foreground and background information. Foreground information of the document image are then considered in the form of foreground patches for computing image quality. For each foreground patch, corresponding gradient maps, obtained from the reference and distorted gradient magnitude maps, are used to compute a gradient magnitude similarity map of the patch. Gradient magnitude similarity deviation of the patch is then calculated by the means of standard deviation over all the values in the gradient magnitude similarity map obtained for the patch. An average pooling is finally performed on all the standard deviations obtained for all the foreground patches to obtain the final image quality metric of the distorted document image. To evaluate the proposed method, we used 3 different datasets. The first dataset was a dataset composed of 377 document images of which 29 were reference images and 348 were distorted images. The other datasets were LIVE and CSIQ datasets composed of scene images with MHOS as ground truth. The results obtained from the proposed system are encouraging.

Find in your library

Share

COinS