Photometric and geometric restoration of document images using inpainting and shape-from-shading

Abstract

The popularity of current hand-held digital imaging devices such as camera phones, PDAs, camcorders has promoted the use of digital cameras to capture document images for daily information recording purpose. However, the captured images often contain photometric and geometric distortions when the documents are of non-planar shapes, which cause significant problems to various document image analysis (DIA) tasks such as OCR. In this paper, we propose a restoration framework that removes both photometric and geometric distortions in smoothly warped document images to facilitate human perception and machine recognition. First, the photometric distortions are corrected by separating the shading image from the reflectance image using inpainting and surface fitting techniques. Next, a 2-pass Shape-from-Shading (SFS) method is exploited to recover the document's surface shape based on the extracted shading image. Once the document's shape is obtained, the geometric distortions are rectified through a physically-based flattening process. Experiments on real document images show the performance of each sub-task and demonstrate a complete solution to the restoration of physically-distorted document images. Copyright © 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.