User-assisted ink-bleed reduction
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Visual enhancement of old documents with hyperspectral imaging
Pattern Recognition
A real-world noisy unstructured handwritten notebook corpus for document image analysis research
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
A ground truth bleed-through document image database
TPDL'12 Proceedings of the Second international conference on Theory and Practice of Digital Libraries
Nonrigid recto-verso registration using page outline structure and content preserving warps
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
F-measure as the error function to train neural networks
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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We present a new method for blind document bleed-through removal based on separate Markov Random Field (MRF) regularization for the recto and for the verso side, where separate priors are derived from the full graph. The segmentation algorithm is based on Bayesian Maximum a Posteriori (MAP) estimation. The advantages of this separate approach are the adaptation of the prior to the contents creation process (e.g., superimposing two handwritten pages), and the improvement of the estimation of the recto pixels through an estimation of the verso pixels covered by recto pixels; moreover, the formulation as a binary labeling problem with two hidden labels per pixels naturally leads to an efficient optimization method based on the minimum cut/maximum flow in a graph. The proposed method is evaluated on scanned document images from the 18th century, showing an improvement of character recognition results compared to other restoration methods.