A Mask-Based Enhancement Method for Historical Documents

This paper proposes a novel method for document enhancement. The method is based on the combination of two state-of-the-art filters through the construction of a mask. The mask is applied to a TV (Total Variation) -regularized image where background noise has been reduced. The masked image is then filtered by NLmeans (Non-Local Means) which reduces the noise in the text areas located by the mask. The document images to be enhanced are real historical documents from several periods which include several defects in their background. These defects result from scanning, paper aging and bleed-through. We observe the improvement of this enhancement method through OCR accuracy

OCR Accuracy Improvement Through a PDE-based Approach

International audienceThis paper focuses on improving the optical character recognition (OCR) system 's accuracy by restoring damaged character through a PDE (Partial Differential Equation)-based approach. This approach, proposed by D. Tschumperle, is an anisotropic diffusion approach driven by local tensors fields. Actually, such approach has many useful properties that are relevant for use in character restoration. For instance, this approach is very appropriate for the processing of oriented patterns which are major characteristics of textual documents. It incorporates both edge enhancing diffusion that tends to preserve local structures during smoothing and coherence-enhancing diffusion that processes oriented structures by smoothing along the flow direction. Furthermore, this tensor diffusion-based approach compared to the existing sate of the art requires neither segmentation nor training steps. Some experiments, done on degraded document images, illustrate the performance of this PDE-based approach in improving both of the visual quality and the OCR accuracy rates for degraded document images