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A method of recognition of arabic cursive handwriting
1Department of Information and Computer Science, University of Petroleum and Minerals, Dhahran, Saudi Arabia.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Summary
This study introduces a novel structural method for recognizing handwritten Arabic characters by segmenting words into strokes and analyzing their properties. The approach achieves high accuracy in machine recognition of Arabic script.
Area of Science:
- Computer Science
- Artificial Intelligence
- Pattern Recognition
Background:
- Significant advancements in machine recognition for Latin, Kana, and Chinese characters over two decades.
- Limited progress in machine recognition of Arabic characters, particularly handwritten forms.
Purpose of the Study:
- To propose a novel structural recognition method for Arabic cursively handwritten words.
- To address the gap in automated Arabic character recognition technology.
Main Methods:
- Word segmentation into individual strokes.
- Classification of strokes based on geometrical and topological features.
- Analysis of relative stroke positions for character reconstruction and word recognition.
Main Results:
- Experimental validation demonstrated high recognition accuracy on handwritten Arabic texts.
- The proposed structural method effectively combines classified strokes into recognized words.
Conclusions:
- The developed structural recognition method shows significant promise for Arabic character recognition.
- This technique offers a viable solution for the previously under-addressed challenge of automated Arabic handwriting recognition.
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