This paper presents a restoration framework for correcting both photometric and geometric distortions in camera-based images of non-planar shaped documents to facilitate human perception and machine recognition. The photometric distortions, usually perceived as shading artifacts, are corrected by separating the shading image from the reflectance image through digital inpainting and surface fitting techniques. Meanwhile, the extracted shading image is also used to recover the document's surface shape through a shape-from-shading (SFS) method with a generic formulation of the image irradiance under arbitrary illumination conditions. The recovered surface shape is then employed to correct the geometric distortions through a physically-based flattening process. Results on real document images demonstrate the performance of each sub-task and the functionality of the whole framework. OCR results on restored images also show great improvements over the original distorted images.
['Li Zhang', 'Andy M. Yip', 'Chew Lim Tan']
A Restoration Framework for Correcting Photometric and Geometric Distortions in Camera-based Document Images