Contextual Restoration of Severely Degraded Document Images

We propose an approach to restore severely degraded document images using a probabilistic context model. Un- like traditional approaches that use previously learned prior models to restore an image, we are able to learn the text model from the degraded document itself, making the approach independent of script, font, style, etc. We model the contextual relationship using an MRF. The ability to work with larger patch sizes allows us to deal with severe degradations including cuts, blobs, merges and vandalized documents. Our approach can also integrate document restoration and super-resolution into a single framework, thus directly generating high quality images from degraded documents. Experimental results show significant improve- ment in image quality on document images collected from various sources including magazines and books, and com- prehensively demonstrate the robustness and adaptability of the approach. It works well with document collections such as books, e...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Jyotirmoy Banerjee, Anoop M. Namboodiri, C. V. Jawahar
• Keywords: Computer Vision, Contextual Restoration, CVPR 2009, Degraded Document Images