Title :
Revealing the Visually Unknown in Ancient Manuscripts with a Similarity Measure for IR-Imaged Inks
Author :
Licata, Aaron ; Psarrou, Alexandra ; Kokla, Vassiliki
Author_Institution :
CVIR Res. Lab., Univ. of Westminster, Harrow, UK
Abstract :
One of the tasks facing historians and conservationists is the authentication or dating of medieval manuscripts. To this end it is important to them to verify whether writings on the same or different manuscripts are concurrent. In this work we explore this task by capturing images of manuscript pages in infrared (IR) and modelling and then comparing the ink appearance of segmented text. The modelling of the text appearance relies on the unsupervised multimodal clustering of ink descriptors and the derived probability density functions. The similarity measure is built around the distribution of cluster labels and their proportions. We demonstrate our method by using both model inks of known composition and authentic Byzantine manuscripts.
Keywords :
art; document image processing; feature extraction; history; image classification; image colour analysis; image segmentation; image texture; infrared imaging; ink; pattern clustering; statistical distributions; text analysis; IR-imaged ink; ancient Byzantine manuscript image capture; art conservation; cluster label distribution; feature extraction; history; image colour analysis; infrared imaging; ink descriptor; medieval manuscript authentication; medieval manuscript dating; probability density function; segmented text; similarity measure; text ink appearance modelling; texture recognition classifier; unsupervised multimodal clustering; visually unknown composition; Art; Authentication; Computer vision; Image segmentation; Infrared imaging; Ink; Materials testing; Probability density function; Text analysis; Writing; Document image processing; Image Analysis; Ink Type Modelling; feature extraction;
Conference_Titel :
Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-4500-4
Electronic_ISBN :
1520-5363
DOI :
10.1109/ICDAR.2009.49