Earticle

초록

영어

The feature extraction and classification method(s) used to recognize handwritten characters play an important role in Handwritten Character Recognition applications. A suitable feature extractor and a good classifier play a very important role in achieving high recognition rate for a recognition system. If we want to develop a new feature extractor for a script, it will help us if we have the knowledge of the recognition ability of the existing feature extractor. Kannada is a major south Indian script spoken by about 50 million people. This paper examines a variety of feature extraction approaches and classification methods which have been used in various Optical Character Recognition applications which are designed to recognize handwritten numerals of Kannada script. The study has been conducted using 8 different features computed from zonal extraction, image fusion, radon transform, fan beam projections, directional chain code, discrete fourier transform, run length count and curvelet transform along with ten different classifiers like Euclidean distance, Chebyshev distance, Manhattan (city block) distance, Cosine distance, K-NN, K-means, K-medoids, Linear classifier, Artificial Immune system and Classifier fusion are considered.

목차

Abstract
 1. Introduction
 2. Description of the Kannada Script
 3. Data Set and Preprocessing
 4. Feature Extraction
  4.1. Zonal based Feature Extraction
  4.2. Image Fusion
  4.3. Radon Transform
  4.4. Fan Beam Projection
  4.5. Directional Features
  4.6. Discrete Fourier Transform
  4.7. Run Length Count
  4.8. The Curvelet Transform
 5. Classification
  5.1. Euclidean Distance Metric
  5.2. Chebyshev Distance Metric
  5.3. Manhattan Distance Metric
  5.4. Cosine Distance Metric
  5.5. Clustering
  5.6. K-Nearest Neighbor
  5.7. Linear Classifier
  5.8. Artificial Immune System
  5.9. Classifier Fusion
 6. Study of some Significant Factors
  6.1. Recognition Accuracy of Different Feature Extraction Methods with Different Classifiers
  6.2. Recognition Accuracy of same Feature with Different Classifier
  6.3. Recognition Accuracy of Different Features with same Classifier
  6.4. Recognition Accuracy of Different Variations of the Feature Extraction Method with Different Options of the Classifier
  6.5. Effect of Training Dataset Size on Recognition Accuracy
  6.6. Effect of Fusing the Classifier Decision on Recognition Accuracy
 7. Comparative Study
 8. Discussions and Conclusion
 References

키워드

저자

• Mamatha Hosalli Ramappa [ Department of Information Science and Engineering P E S Institute of Technology (West Campus) Bangalore, India ]
• Srikantamurthy Krishnamurthy [ Department of Computer Science and Engineering P E S Institute of Technology (South Campus) Bangalore, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605