A multi-scale framework for adaptive binarization of degraded document images
Pattern Recognition
Multichannel blind separation and deconvolution of images for document analysis
IEEE Transactions on Image Processing
Color space transformations for analysis and enhancement of ancient degraded manuscripts
Pattern Recognition and Image Analysis
Visual enhancement of old documents with hyperspectral imaging
Pattern Recognition
A ground truth bleed-through document image database
TPDL'12 Proceedings of the Second international conference on Theory and Practice of Digital Libraries
A learning framework for the optimization and automation of document binarization methods
Computer Vision and Image Understanding
Generation of learning samples for historical handwriting recognition using image degradation
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
An efficient parametrization of character degradation model for semi-synthetic image generation
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
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In order to tackle problems such as shadow- through and bleed-through, a novel defect model is developed which generates physically damaged document images. This model addresses physical degradation, such as aging and ink seepage. Based on the diffusive nature of the physical defects, the model is designed using virtual diffusion processes. Then, based on this degradation model, a restoration method is proposed and used to fix the bleed-through effect in double-sided document images using the reverse diffusion process. Subjective and objective evaluations are performed on both the degradation model and the restoration method. The experiments show promising results on both real and generated data.