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Proceedings Paper

Feature extraction from printed Persian sub-words using Haar wavelet transform
Author(s): Samira Nasrollahi Dizajyekan; Afshin Ebrahimi
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Paper Abstract

This article presents a novel set of shape descriptors which are especially well-suited for the recognition of printed Persian sub-words based on their holistic shapes. The descriptor set is derived from the wavelet transform of a sub-word's image. The proposed algorithm is used to extract features from 87804 sub-words of 4 fonts and 3 sizes. To evaluate the feature extraction results, this algorithm was used to obtain recognition rate for a set of sub-words in a printed Persian text document. Features of an unknown sub-word are extracted and compared with all sub-words features in the dictionary and the desired sub-word is identified. In this stage to increase the recognition rate, dot features of the unknown sub-word are used as the second feature and compared with dot codes of 10 last sub-words in before stage and the sub-word with maximum similarity is extracted as correct recognized sub-word.

Paper Details

Date Published: 8 July 2011
PDF: 5 pages
Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 80091B (8 July 2011); doi: 10.1117/12.896300
Show Author Affiliations
Samira Nasrollahi Dizajyekan, Sahand Univ. of Technology (Iran, Islamic Republic of)
Afshin Ebrahimi, Sahand Univ. of Technology (Iran, Islamic Republic of)


Published in SPIE Proceedings Vol. 8009:
Third International Conference on Digital Image Processing (ICDIP 2011)
Ting Zhang, Editor(s)

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