Earticle

초록

영어

Handwriting is the most effective way by which civilized people speaks. Devanagari is the basic Script widely used all over India. Many Indian languages like Hindi, Marathi, Rajasthani are based on Devanagari Script. In the proposed work multistage approach i.e. an artificial neural network based classifier and statistical and structural method based feature extraction method has been employed for the recognition of the script. Optical isolated Marathi words are taken as an input image from the scanner. An input image is preprocessed and segmented. The key step is feature extraction, features are extracted in terms of various structural and statistical features like End points, middle bar, loop, end bar, aspect ratio etc. Feature vector is applied to Self organizing map (SOM) which is one of the classifier of an artificial neural Network.SOM is trained for such 3000 different characters collected from 500 persons. The characters are classified into three different classes. The proposed classifier attains 98% - 99% accuracy except special characters.

목차

Abstract
 1. Introduction
  1.1. Pattern Recognition System
 2. Past Review
 3. Recognition of Handwritten Devanagari OCR System
  3.1. Image Acquisition
  3.2. Image Pre-processing
  3.3. RGB to Gray Conversion
  3.4. Noise Removal
  3.5. Segmentation
  3.6. Feature Extraction
  3.6. Classification
  3.7. Post Processing
 4. Performance Analysis
 5. Conclusion and Discussions
 Acknowledgments
 References

키워드

저자

• Vijaya Rahul Pawar [ Research Scholar, BVDU, COE, Pune-411043, Maharashtra, India ]
• Arun Gaikwad [ Dept. of E&TC, DCOE&R,Pune- 411041, Maharashtra, India. ]

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