Earticle

초록

영어

In this study, we propose a flexible template-matching algorithm for word segmentation, and structural analysis of features extraction is used for character recognition in the printed Arabic text. The input text image is preprocessed by the binarization and then by morphological operations. A vector quantization of the thinned image (VQTM) is created based on the idea of a freeman chain code tracking method. In the segmentation process, 113 character templates are compared for partially/completely existence in the VQTM. A non-linear filter is applied on the segmented regions to extract the termination and bifurcation features. The spatial distribution of the extracted features and other statistical characteristics are analyzed for the verification of recognition. Experimental results show that the overall recognition rate of the three fonts: Arabic transparent, simplified Arabic and traditional Arabic is 98.63%.

목차

Abstract
 1. Introduction
 2. System Framework
 3. Word Segmentation
  3.1 Vector Quantization
  3.2. Character Matching
 4. Character Recognition
 5. Experimental Results
 6. Conclusion and Future Work
 References

키워드

저자

• Ihab Zaqout [ Department of Information Technology Faculty of Engineering and Information Technology Al-Azhar University – Gaza, Palestine ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605