Earticle

초록

영어

Optical character recognition is one of the most active branches of pattern recognition deals with different aspects of automatic recognition of written patterns. Among numerous techniques, systems, and software reported in the literature, Persian printed digits classification has not been attended a lot. In this paper, a consistent system for transformation-independent recognition of Persian printed numerals based on Hu moment invariants, which are invariant to translation, rotation, and scale has been introduced. Since utilization of these invariants tackles with some important issues such as noise sensitivity, compactness and invariance to reversal patterns, some operations to compensate these drawbacks have been done. In addition, a robust classifier named fuzzy min-max neural network has been used to encounter such a compact and overlapped feature space. Set of different experiments has been done and results show the proposed system is so successful to invariant classification of Persian printed digits.

목차

Abstract
 1. Introduction
 2. The Proposed System
 3. Preprocessing
 4. Presentation
  4.1. Moment Invariants
  4.2. Feature Vector Enrichment
 5. Classification Using Fuzzy Min-Max Neural Network
  5.1. FMMNN Properties
 6. Implementation and Experimental Results
  6.1. The Utilized Data Set
  6.2. Experiment without any Feature Vector Enrichments
  6.3. Experiment with Feature Vector Enrichment
  6.4. Fuzzy Min-Max Neural Network Parameters
  6.5. Effect of Size of Training Samples
 7. Conclusions
 References

키워드

저자

• Hamid Reza Boveiri [ Member, Young Researchers Club, Islamic Azad University, Shushtar Branch Faculty Member, Sama College, Islamic Azad University, Shushtar Branch ]

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