Pattern Recognition
Generation of images of historical documents by composition
Proceedings of the 2002 ACM symposium on Document engineering
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Historical Document Image Enhancement Using Background Light Intensity Normalization
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
On Foreground-Background Separation in Low Quality Color Document Images
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Graphics Recognition. Recent Advances and New Opportunities
An indexing and retrieval system of historic art images based on fuzzy shape similarity
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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This paper presents a new color document image segmentation system suitable for historical Arabic manuscripts. Our system is composed of a hybrid method which couple together background light intensity normalization algorithm and k-means clustering with maximum likelihood (ML) estimation, for foreground/background separation. Firstly, the background normalization algorithm performs separation between foreground and background. This foreground is used in later steps. Secondly, our algorithm proceeds on luminance and distort the contrast. These distortions are corrected with a gamma correction and contrast adjustment. Finally, the new enhanced foreground image is segmented to foreground/background on the basis of ML estimation. The initial parameters for the ML method are estimated by k-means clustering algorithm. The segmented image is used to produce a final restored document image. The techniques are tested on a set of Arabic historical manuscripts documents from the National Tunisian Library. The performance of the algorithm is demonstrated on by real color manuscripts distorted with show-through effects, uneven background color and localized spot.