Earticle

초록

영어

In a multi script environment, an archive of documents having the text regions printed in different scripts is in practice. For automatic processing of such documents through Optical Character Recognition (OCR), it is necessary to identify different script regions of the document. In this paper, a novel texture-based approach is presented to identify the script type of the collection of documents printed in seven scripts, to categorize them for further processing. The South Indian documents printed in the seven scripts - Kannada, Tamil, Telugu, Malayalam, Urdu, Hindi and English are considered here The document images are decomposed through the Wavelet Packet Decomposition using the Haar basis function up to level two. Gray level co-occurrence matrix is constructed for the coefficient sub bands of the wavelet transform. The Haralick texture features are extracted from the co-occurrence matrix and then used in the identification of the script of a machine printed document. Experimentation conducted involved 2100 text images for learning and 1400 text images for testing. Script classification performance is analyzed using the K-nearest neighbor classifier. The average success rate is found to be 99.68%.

목차

Abstract
 1. Introduction
 2. Wavelet Packet Transform (WPT)
 3. Data Collection
 4. Preprocessing
 5. The proposed Model
  5.1 Feature Extraction
  5.2 Classification
 6. Experimental Results
 7. Conclusion
 References

키워드

저자

• M.C. Padma [ PES College of Engineering, ]
• P. A. Vijaya [ Malnad College of Engineering ]

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