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The design of HMM-based banknote recognition system

  • Shan Gai*
  • , Jiafeng Liu
  • , Peng Liu
  • , Xianglong Tang
  • *Corresponding author for this work
  • School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The banknote recognition system based on hidden Markov models (HMM) is proposed. It is based on the empirical risk minimization (ERM) principle. Image preprocessing includes brightness equalization and tilt correction. In order to satisfy the high speed and reliability of the banknote processing system, the grid segmentation is used for features extraction. Analyze the experimental data and determine the number of states, iterations, and Gaussian components. The proposed banknote recognition system can be applied to classify any kinds of banknotes. More than 16,000 RMB samples are sampled by CIS (Contact Image Sensor) with 25dpi. Experimental results show that the proposed method obtained higher recognition rate than ANN and SVM.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Pages106-110
Number of pages5
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009 - Shanghai, China
Duration: 20 Nov 200922 Nov 2009

Publication series

NameProceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Volume4

Conference

Conference2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Country/TerritoryChina
CityShanghai
Period20/11/0922/11/09

Keywords

  • Banknote recognition
  • ERM
  • Features extraction
  • HMM

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