Independent component analysis: algorithms and applications
Neural Networks
Restoration of Archival Documents Using a Wavelet Technique
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correcting Show-Through Effects on Document Images by Multiscale Analysis
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Color Texture Classification by Normalized Color Space Representation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
A Multiscale Approach to Restoring Scanned Color Document Images with Show-Through Effects
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Independent component analysis for document restoration
International Journal on Document Analysis and Recognition
A quantitative method for assessing algorithms to remove back-to-front interference in documents
Proceedings of the 2007 ACM symposium on Applied computing
Low quality document image modeling and enhancement
International Journal on Document Analysis and Recognition
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
IEEE Transactions on Image Processing
On independent color space transformations for the compression of CMYK images
IEEE Transactions on Image Processing
Show-through cancellation in scans of duplex printed documents
IEEE Transactions on Image Processing
A ground truth bleed-through document image database
TPDL'12 Proceedings of the Second international conference on Theory and Practice of Digital Libraries
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In this paper we focus on ancient manuscripts, acquired in the RGB modality, which are degraded by the presence of complex background textures that interfere with the text of interest. Removing these artifacts is not trivial, especially with ancient originals, where they are usually very strong. Rather than applying techniques to just cancel out the interferences, we adopt the point of view of separating, extracting and classifying the various patterns superimposed in the document. We show that representing RGB images in different color spaces can be effective for this goal. In fact, even if the RGB color representation is the most frequently used color space in image processing, it does not maximize the information contents of the image. Thus, in the literature, several color spaces have been developed for analysis tasks, such as object segmentation and edge detection. Some color spaces seem to be particularly suitable to the analysis of degraded documents, allowing for the enhancement of the contents, the improvement of the text readability, the extraction of partially hidden features, and a better performance of thresholding techniques for text binarization. We present and discuss several examples of the successful application of both fixed color spaces and self-adaptive color spaces, based on the decorrelation of the original RGB channels. We also show that even simpler arithmetic operations among the channels can be effective for removing bleed-through, refocusing and improving the contrast of the foreground text, and to recover the original RGB appearance of the enhanced document.