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Robust directional features for wordspotting in degraded Syriac manuscripts

Petra Bilane 1 Stéphane Bres 1 Hubert Emptoz 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : This paper presents a contribution to Word Spotting applied for digitized Syriac manuscripts. The Syriac language was wrongfully accused of being a dead language and has been set aside by the domain of handwriting recognition. Yet it is a very fascinating handwriting that combines the word structure and calligraphy of the Arabic handwriting with the particularity of being intentionally written tilted by an angle of approximately 45°. For the spotting process, we developed a method that should find all occurrences of a certain query word image, based on a selective sliding window technique, from which we extract directional features and afterwards perform a matching using Euclidean distance correspondence between features. The proposed method does not require any prior information, and does not depend of a word to character segmentation algorithm which would be extremely complex to realize due to the tilted nature of the handwriting.
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Submitted on : Tuesday, September 26, 2017 - 12:31:40 PM
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Petra Bilane, Stéphane Bres, Hubert Emptoz. Robust directional features for wordspotting in degraded Syriac manuscripts. International Workshop on Content Based Multimedia Indexing (CBMI 08), Jun 2008, London, United Kingdom. pp.526-533, ⟨10.1109/CBMI.2008.4564992⟩. ⟨hal-01593480⟩



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