Multi-channel filtering techniques for texture segmentation and surface quality inspection
Multi-channel filtering techniques for texture segmentation and surface quality inspection
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Ink Texture Analysis for Writer Identification
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Historical Document Image Enhancement Using Background Light Intensity Normalization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
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Identification of Non-Black Inks Using HSV Colour Space
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Ink Discrimination Based on Co-occurrence Analysis of Visible and Infrared Images
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
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One of the tasks facing historians and preservationists is the authentication or dating of medieval manuscripts. To this end it is important to verify whether writings on the same or different manuscripts are concurrent. We propose a novel approach for the automated image-based differentiation of inks used in medieval manuscripts. We consider the problem of capturing images of manuscript pages in near-infrared (NIR) spectrum and compare the ink appearance and textural features of segmented text. We present feature descriptors that capture the variability of the visual properties of the inks in NIR based on intensity distributions of histograms and co-occurrence matrices. Our approach is novel as it is entirely image based and does not include the spectrum analysis of the inks. The method is validated by using model ink images manufactured based on known recipes and ink segmented from medieval manuscripts dated from the 11th to the 16th century. Model inks are classified by using both supervised and unsupervised clustering. Comparison of inks of unknown composition is achieved through unsupervised multi-dimensional clustering of the feature descriptors and similarity measures of derived probability density functions.