Abstract
Handwriting recognition in the Arabic language is considered one of the most challenging problems and the accuracies in recognizing still need more enhancements due to the Arabic character’s nature, cursive writing, style, and size of writing in contrast to working with other languages. In this paper, we propose a system for Arabic Offline Handwritten Character Recognition based on Naïve Bayes classifier (NB). Extraction features preceded by divided the image of character into three horizontal and vertical zones and 3x3 zones in one and two dimensions respectively, then classified by Naïve Bayes. The performance of the system proposes evaluated by using the benchmark CENPARMI database reached up to 97.05% accuracy rate. Experimental results confirm a high enhancement inaccuracy rate in comparison with other Arabic Optical Character Recognition systems.
Original language | English |
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Title of host publication | 2021 International Conference on Computing and Communications Applications and Technologies (I3CAT) |
Subtitle of host publication | conference proceedings |
Publisher | IEEE |
Pages | 82-87 |
Number of pages | 6 |
ISBN (Electronic) | 9781665424417 |
ISBN (Print) | 9781665424424 |
DOIs | |
Publication status | Published - 7 Dec 2021 |
Event | 2021 International Conference on Computing and Communications Applications and Technologies - virtual conference, Ipswich, United Kingdom Duration: 15 Sep 2021 → 15 Sep 2021 https://i3cat.org/ |
Conference
Conference | 2021 International Conference on Computing and Communications Applications and Technologies |
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Abbreviated title | I3CAT |
Country/Territory | United Kingdom |
City | Ipswich |
Period | 15/09/21 → 15/09/21 |
Internet address |
Keywords
- Arabic handwritten character
- Starters and intersections
- Minor-starters
- Naïve Bayes classifier