Home > Archive > 2013 > Volume 3 Number 4 (Aug. 2013) >
IJMLC 2013 Vol.3(4): 384-388 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.344

Instant Arabic Translation System for Signboard Images Based on Printed Character Recognition

Rafeeq Abdul Rahman A. Al-Hashemi and Shoroq Almamon Alsharari

Abstract—Daily lives contain many of the menus and signboards which carry important information, but sometimes it may cause problems when we cannot understand it. This paper aimed to develop a new system that translates Arabic texts of the signboards into English text by using mobile phone camera. In spite of the diversity of translation products of text embedded in images for many languages, Arabic texts seem to be not yet well solved to address this problem. The system will automatically translate the Arabic text embedded in images into English language. Four subsystems used in the algorithm: preprocessing, segmentation (text detection), character recognition and translation. Dealing with Arabic language was the most important problem faced by the proposed system because it has a set of characteristics makes the identification very difficult, such as words interrelated. The system automatically detects the text and works well with different backgrounds, rotated images, skewed, font sizes and blurred images. The system was assessed using recall measurement to evaluate the performance of the developed system and the experimental results of character recognition show a rate of 81.82%, the word recognition subsystem gave a rate of (94.44%) and the word translation was about (83.33%).

Index Terms—Arabic translation, information extraction, character recognition.

The authors are with Al-Hussein Bin Talal University (AHU), Jordan (e-mail: Rafiq_alhashimy@yahoo.com).


Cite:Rafeeq Abdul Rahman A. Al-Hashemi and Shoroq Almamon Alsharari, "Instant Arabic Translation System for Signboard Images Based on Printed Character Recognition," International Journal of Machine Learning and Computing vol.3, no. 4, pp. 384-388, 2013.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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