An online numeral recognition system using improved structural features – A unified method for handwritten Arabic and Persian numerals

Jaafar M. Alghazo, Ghazanfar Latif, Ammar Elhassan, Loay Alzubaidi, Ahmad Al-Hmouz, Rami Al-Hmouz

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


With the advances in machine learning techniques, handwritten recognition systems also gained importance. Though digit recognition techniques have been established for online handwritten numerals, an optimized technique that is writer independent is still an open area of research. In this paper, we propose an enhanced unified method for the recognition of handwritten Arabic and Persian numerals using improved structural features. A total of 37 structural based features are extracted and Random Forest classifier is used to classify the numerals based on the extracted features. The results of the proposed approach are compared with other classifiers including Support Vector Machine (SVM), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN). Four different well-known Arabic and Persian databases are used to validate the proposed method. The obtained average 96.15% accuracy in recognition of handwritten digits shows that the proposed method is more efficient and produces better results as compared to other techniques.

Original languageEnglish
Pages (from-to)33-40
Number of pages8
JournalJournal of Telecommunication, Electronic and Computer Engineering
Issue number2-10
Publication statusPublished - 2017
Externally publishedYes


  • Arabic Digits
  • Arabic Numerals
  • Digit Recognition
  • Numerals Recognition
  • Persian Digits
  • Persian Numerals
  • Random Forest
  • Structural Features

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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