A robust statistical set of features for Amazigh handwritten characters
| Autor: | Abdellatif Dahmouni, Nabil Aharrane, Khalid Satori, K. El Moutaouakil |
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| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science 020206 networking & telecommunications Pattern recognition 02 engineering and technology Computer Graphics and Computer-Aided Design Image (mathematics) Set (abstract data type) Statistical classification Character (mathematics) Histogram Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Projection (set theory) Character recognition |
| Zdroj: | Pattern Recognition and Image Analysis. 27:41-52 |
| ISSN: | 1555-6212 1054-6618 |
| DOI: | 10.1134/s1054661817010011 |
| Popis: | The main problem in the handwritten character recognition systems (HCR) is to describe each character by a set of features that can distinguish it from the other characters. Thus, in this paper, we propose a robust set of features extracted from isolated Amazigh characters based on decomposing the character image into zones and calculate the density and the total length of the histogram projection in each zone. In the experimental evaluation, we test the proposed set of features, to show its performance, with different classification algorithms on a large database of handwritten Amazigh characters. The obtained results give recognition rates that reach 99.03% which we presume good and satisfactory compared to other approaches and show that our proposed set of features is useful to describe the Amazigh characters. |
| Databáze: | OpenAIRE |
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