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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Graphical Models: Methods for Data Analysis and Mining
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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CGIV '05 Proceedings of the International Conference on Computer Graphics, Imaging and Visualization
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IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal on Document Analysis and Recognition
OCR Accuracy Improvement through a PDE-Based Approach
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
User-assisted ink-bleed correction for handwritten documents
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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IEEE Transactions on Image Processing
Stroke-model-based character extraction from gray-level document images
IEEE Transactions on Image Processing
The FastICA Algorithm Revisited: Convergence Analysis
IEEE Transactions on Neural Networks
A multi-scale framework for adaptive binarization of degraded document images
Pattern Recognition
IBN SINA: a database for research on processing and understanding of Arabic manuscripts images
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Text enhancement by PDE's based methods
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
A learning framework for the optimization and automation of document binarization methods
Computer Vision and Image Understanding
Historical document image restoration using multispectral imaging system
Pattern Recognition
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This paper addresses the problem of enhancing and restoring single-sided low-quality single-sided document images. Initially, a series of multi-level classifiers is introduced covering several levels, including the regional and content levels. These classifiers can then be integrated into any enhancement or restoration method to generalize or improve them. Based on these multi-level classifiers, we first propose a novel PDE-based method for the restoration of the degradations in single-sided document images. To reduce the local nature of PDE-based methods, we empower our method with two flow fields to play the role of regional classifiers and help in preserving meaningful pixels. Also, the new method further diffuses the background information by using a content classifier, which provides an efficient and accurate restoration of the degraded backgrounds. The performance of the method is tested on both real samples, from the Google Book Search dataset, UNESCO's Memory of the World Programme, and the Juma Al Majid (Dubai) datasets, and synthesized samples provided by our degradation model. The results are promising. The method-independent nature of the classifiers is illustrated by modifying the ICA method to make it applicable to single-sided documents, and also by providing a Bayesian binarization model.