Ranking Segmentation Paths Using Fuzzified Decision Rules

  • Zhongkang Lu
  • Zheru Chi
  • Pengfei Shi
  • Eam Khwang Teoh
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 52)


Connected characters are usually recognized using a segmentation-based approach in which individual characters are segmented first by segmentation paths before they are fed to a character classifier. Good ranking of segmentation paths can significantly shorten the processing time in evaluating segmentation paths and improve the recognition rate by avoiding misinterpretation. In this chapter, we present a method of using fuzzified decision rules to rank segmentation paths. The fuzzified decision rules are obtained by fuzzifying the decision rules extracted from a decision tree learned from examples. Nine measures of the properties of a segmentation path are extracted as the input and the centroid defuzzification method is adopted to defuzzify the output. Our method of ranking segmentation paths has been tested on the NIST Special Database 3 with good results.


Decision Tree Membership Function Decision Rule Feature Point Recognition Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Zhongkang Lu
    • 1
  • Zheru Chi
    • 2
  • Pengfei Shi
    • 3
  • Eam Khwang Teoh
    • 1
  1. 1.School of Electrical and Electronic Engineering Intelligent Machines Research LaboratoryNan Yang Technological UniversitySingapore
  2. 2.Department of Electronic and Information EngineeringThe Hong Kong Polytechnic UniversityHung Hom, Kowloon, Hong KongChina
  3. 3.Institute of Pattern Recognition and Image ProcessingShanghai Jiaotong UniversityShanghaiP.R. of China

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