Publications:Ethiopic Character Recognition Using Direction Field Tensor

From ISLAB/CAISR
Revision as of 12:50, 13 March 2014 by SlawekBot (talk | contribs) (Created page with "<div style='display: none'> == Do not edit this section == </div> {{PublicationSetupTemplate|Author=Yaregal Assabie, Josef Bigun |PID=239341 |Name=Assabie, Yaregal (Addis Abab...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigationJump to search

Do not edit this section

Property "Publisher" has a restricted application area and cannot be used as annotation property by a user. Property "Author" has a restricted application area and cannot be used as annotation property by a user. Property "Author" has a restricted application area and cannot be used as annotation property by a user.

Keep all hand-made modifications below

Title Ethiopic Character Recognition Using Direction Field Tensor
Author
Year 2006
PublicationType Conference Paper
Journal
HostPublication he 18th International Conference on Pattern Recognition : proceedings : 20-24 August, 2006, Hong Kong
Conference 18th International Conference on Pattern Recognition, ICPR 2006, Hong Kong, 20 - 24 August, 2006
DOI http://dx.doi.org/10.1109/ICPR.2006.507
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239341
Abstract

Many languages in Ethiopia use a unique alphabet called Ethiopic for writing. However, there is no OCR system developed to date. In an effort to develop automatic recognition of Ethiopic script, a novel system is designed by applying structural and syntactic techniques. The recognition system is developed by extracting primitive structural features and their spatial relationships. A special tree structure is used to represent the spatial relationship of primitive structures. For each character, a unique string pattern is generated from the tree and recognition is achieved by matching the string against a stored knowledge base of the alphabet. To implement the recognition system, we use direction field tensor as a tool for character segmentation, and extraction of structural features and their spatial relationships. Experimental results are reported.