Degraded character image restoration using active contours: a first approach
Proceedings of the 2002 ACM symposium on Document engineering
Towards Automatic Transcription of Syriac Handwriting
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten Syriac Character Recognition using Order Structure Invariance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Scale Space Approach for Automatically Segmenting Words from Historical Handwritten Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenspace Method for Text Retrieval in Historical Document Images
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Automatic accurate broken character restoration for patrimonial documents
International Journal on Document Analysis and Recognition
Adaptive degraded document image binarization
Pattern Recognition
Word spotting for historical documents
International Journal on Document Analysis and Recognition
International Journal on Document Analysis and Recognition
Text search for medieval manuscript images
Pattern Recognition
Locality Sensitive Pseudo-Code for Document Images
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Retrieval from document image collections
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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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.