A feature extraction method for Arabic Offline Handwritten Recognition System using Naïve Bayes classifier

Ahmed Subhi Abdalkafor, Khattab M. Ali Alheeti, Laith Al-Jobouri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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 languageEnglish
Title of host publication2021 International Conference on Computing and Communications Applications and Technologies (I3CAT)
Subtitle of host publicationconference proceedings
PublisherIEEE
Pages82-87
Number of pages6
ISBN (Electronic)9781665424417
ISBN (Print)9781665424424
DOIs
Publication statusPublished - 7 Dec 2021
Event2021 International Conference on Computing and Communications Applications and Technologies - virtual conference, Ipswich, United Kingdom
Duration: 15 Sep 202115 Sep 2021
https://i3cat.org/

Conference

Conference2021 International Conference on Computing and Communications Applications and Technologies
Abbreviated titleI3CAT
Country/TerritoryUnited Kingdom
CityIpswich
Period15/09/2115/09/21
Internet address

Keywords

  • Arabic handwritten character
  • Starters and intersections
  • Minor-starters
  • Naïve Bayes classifier

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