Difference between revisions of "Publications:Online Handwriting Recognition of Ethiopic Script"
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| − | |Name=Assabie, Yaregal (Addis Ababa University, Department of Computer Science, Addis Ababa, Ethiopia);Bigun, Josef | + | |Name=Assabie, Yaregal (Addis Ababa University, Department of Computer Science, Addis Ababa, Ethiopia);Bigun, Josef (josef) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Intelligenta system (IS-lab) (3941)) |
|Title=Online Handwriting Recognition of Ethiopic Script | |Title=Online Handwriting Recognition of Ethiopic Script | ||
|PublicationType=Conference Paper | |PublicationType=Conference Paper | ||
Latest revision as of 21:41, 30 September 2016
| Title | Online Handwriting Recognition of Ethiopic Script |
|---|---|
| Author | Yaregal Assabie and Josef Bigun |
| Year | 2008 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Proceedings : Eleventh International Conference on Frontiers in Handwriting Recognition, Montréal, Québec - Canada, August 19-21, 2008 |
| Conference | Eleventh International Conference on Frontiers in Handwriting Recognition (ICFHR2008), August 19-21, Montreal, Quebec, Canada |
| DOI | |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:408386 |
| Abstract | Online recognition of handwritten characters is gaining a renewed interest as it provides a natural way of data entry for a wide variety of handheld devices. In this paper, we present online handwriting recognition system for Ethiopic script based on the structural and syntactical analysis of the strokes forming characters. The complex structures of characters are represented by the spatio- temporal relationships of simple-shaped strokes called primitives. A special tree structure is used to model spatio- temporal relationships of the strokes. The tree generates a unique set of primitive stroke sequences for each character, and for recognition each stroke sequence is matched against a stored knowledge base. Characters are also classified based on their structural similarity to select a plausible set of characters for un unknown input, which improves recognition and processing time. We also present a dataset collected for training and testing online recognition systems for Ethiopic script. The dataset is prepared in accordance with the international standard UNIPEN format. The recognition system is tested with the collected dataset and experimental results are reported. |
