Earticle

초록

영어

The world heritage of handwritten Arabic documents is huge however only manual indexing and retrieval techniques of the content of these documents are available. To facilitate an automatic retrieval of such hand-written Arabic document, a number of automatic recognition systems for handwritten Arabic words have been proposed. Nevertheless, these systems suer from low recognition accuracy due to the peculiarities of the handwritten Arabic language. Thus, in this Paper we propose a segmentation based recognition system for handwritten Arabic words. We divide a handwritten word into smaller pieces of a word and then these small pieces are segmented into candidate letters. These candidate letters are converted into their correspondence chain-code representation. Thereafter we extract discrete, statistical and structural features for classication. Additionally, we introduce a novel active contour based feature to increase the recognition accuracy of strongly deformed Arabic letters. We also use a decision tree to reduce the number of potential classes. We then use a neural network to compute weights for all statistical features and use them as input for a k-NN classier. Our experiments show that the extracted features by our technique achieve higher recognition accuracy as compared to other features.

목차

Abstract
 1 Introduction
 2 Related Works
 3 Proposed Technique
  3.1 Pre-Processing
  3.2 Feature Extraction
 4 Recognition
  4.1 Pre-Classication
  4.2 Classication
  4.3 Post-Classication
 5 Experimental Results & Discussion
 6 Conclusion
 References

키워드

저자

• Laslo Dinges [ Institute for Electronics, Signal Processing and Communications (IESK) Otto-von-Guericke-University Magdeburg; Germany ]
• Ayoub Al-Hamadi [ Institute for Electronics, Signal Processing and Communications (IESK) Otto-von-Guericke-University Magdeburg; Germany ]
• Moftah Elzobi [ Institute for Electronics, Signal Processing and Communications (IESK) Otto-von-Guericke-University Magdeburg; Germany ]
• Zaher Al Aghbari [ Department of Computer Science University of Sharjah; UAE ]
• Hassan Mustafa [ Faculty of Engineering Albaha University; Saudi Arabia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605