Performance Evaluation of Multiple Image Binarization Algorithms Using Multiple Metrics on Standard Image Databases

  • Sudipta Roy
  • Sangeet Saha
  • Ayan Dey
  • Soharab Hossain Shaikh
  • Nabendu Chaki
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)


The area of image binarization has matured to a significant extent in last few years. There has been multiple, well-defined metrics for quantitative performance estimation of the existing techniques for binarization. However, it stills remains a problem to benchmark one binarization technique with another as different metrics are used to establish the comparative edges of different binarization approaches. In this paper, an experimental work is reported that uses three different metrics for quantitative performance evaluation of seven binarization techniques applied on four different types of images: Arial, Texture, Degraded text and MRI. Based on visually and experimentally the most appropriate methods for binarization of images have been identified for each of the four classes under consideration. We have used standard image databases along with the archived reference images, as available, for experimental purpose.


Iterative Partitioning method Image Thresholding Reference Image Misclassification Error Relative Foreground Area Error 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sudipta Roy
    • 1
  • Sangeet Saha
    • 2
  • Ayan Dey
    • 2
  • Soharab Hossain Shaikh
    • 2
  • Nabendu Chaki
    • 1
  1. 1.Department of Computer Science & EngineeringUniversity of CalcuttaKolkataIndia
  2. 2.A.K. Choudhury School of Information TechnologyUniversity of CalcuttaKolkataIndia

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