Research Article Open Access

Fuzzy ARTMAP Approach for Arabic Writer Identification using Novel Features Fusion

Tanzila Saba1
  • 1 Prince Sultan University, Saudi Arabia


Arabic writer identification and associated tasks are still fresh due to huge variety of Arabic writer's styles. Current research presents a fusion of statistical features, extracted from fragments of Arabic handwriting samples to identify the writer using fuzzy ARTMAP classifier. Fuzzy ARTMP is supervised neural model, especially suited to classification problems. It is faster to train and need less number of training epochs to "learn" from input data for generalization. The extracted features are fed to Fuzzy ARTMP for training and testing. Fuzzy ARTMAP is employed for the first time along with a novel fusion of statistical features for Arabic writer identification.  The entire IFN/ENIT database is used in experiments such that 75% handwritten Arabic words from 411 writers are employed in training and 25% for testing the system at random. Several combinations of extracted features are tested using fuzzy ARTMAP classifier and finally one combination exhibited promising accuracy of 94.724% for Arabic writer identification on IFN/ENIT benchmark database.

Journal of Computer Science
Volume 14 No. 2, 2018, 210-220


Submitted On: 24 October 2017 Published On: 7 February 2018

How to Cite: Saba, T. (2018). Fuzzy ARTMAP Approach for Arabic Writer Identification using Novel Features Fusion. Journal of Computer Science, 14(2), 210-220.

  • 7 Citations



  • Arabic writer's Identification
  • Features Extraction
  • Features Selection
  • Fuzzy ARTMAP
  • IFN/ENIT Database