Earticle

초록

영어

The OCR technology for Indian documents is in emerging stage and most of these Indian OCR systems can read the documents written in only a single script. As many commercial and official documents of different states of India are tri-lingual in nature, therefore identification of script and/ or language is one of the elementary tasks for multi-script document recognition. A script recognizer simplifies the task of multi-lingual OCR by improving the accuracy and reducing the computational complexity. This script recognition may be at line, word or character level depending on interlacing of different scripts at different levels. This paper presents the effectiveness of Gabor Filter banks with kNN, SVM and PNN classifiers to identify the scripts at line level from such trilingual documents. The experiments show that Gabor features with SVM classifier achieve a recognition rate of 99.85% for trilingual documents.

목차

Abstract
 1. Introduction
 2. Feature Extraction
  2.1. 2 Dimensional Gabor Function
  2.2 Gabor filter Design and feature extraction
 3. Classification
  3.1 kNN Classifier
  3.2 SVM Classifier
  3.3 PNN Classifier
 4. Experimental Results and Discussions
  4.1 Data Set
  4.2 Results and Discussions
 5. Conclusion
 References

키워드

저자

• Rajneesh Rani [ Dr B R Ambedkar National Institute of Technology Jalandhar, Jalandhar - 144011, India ]
• Renu Dhir [ Dr B R Ambedkar National Institute of Technology Jalandhar, Jalandhar - 144011, India ]
• Gurpreet Singh Lehal [ Departement of Computer Science and Engineering, Punjabi University Patiala - 144011, India ]

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