Earticle

초록

영어

This paper investigates Farsi handwritten word recognition using common features. Also we applied biologically inspired features (BIFs), derived from a feed forward model of object recognition pathway in visual cortex for Farsi handwritten word recognition problem. Experimental results show that the model achieves high recognition percentage even for large variations and applicability of these features in Small Sample Size problems (SSS).The experiments were achieved using the Iranshahr dataset. This dataset consist of 780 samples of 30 city names of Iran which 600 samples for train and 180 samples for test was used. A set of experiments were conducted to compare Decision Templates with some combination rules. Results show that template based fusion method is superior to the other schemes.

목차

Abstract
 1. Introduction
 2. Feature Extraction
  2.1. Histogram
  2.2. Characteristic Loci
  2.3. Zoning
  2.4. Wavelet Transform
  2.5. Discrete Cosine Transform
  2.6. Gradient
  2.7. Cross Count
  2.8. Principle Component Analysis
 3. Biological Feature Extraction Method
  3.1. Image Layer
  3.2. S1 and C1Layers
  3.3. S2 andC2 Layers
  3.4. C3 andS3 Layers
  3.5. S2b and C2b Layers
 4. K-Nearest Neighbor Classification
 5. Experimental Results
  5.1. Experiment I
  5.2. Experiment II
  5.3. Experiment III
 6. Conclusions
 References

키워드

저자

• Reza Ebrahimpour [ Department of Electrical Engineering, Shahid Rajaee Teachers Training University ]
• Mona Amini [ Department of Mechatronics Engineering, Islamic Azad University, South Tehran ]
• Afra Vahidi Shams [ Department of Mechatronics Engineering, Islamic Azad University, South Tehran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605