Ensemble classifier with dividing training scheme for Chinese scene character recognition

Long Jiang, Hideaki Goto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Scene character recognition problem has attracted a great attention in computer vision field. As one of the most widely used characters, Chinese characters are more complicated than European characters, especially when it comes to those characters appeared in various scene texts. The recognition of Chinese scene characters is still far from a satisfactory level owing to the lack of adequate training data and efficient learning methods. In this paper, we propose a new strategy to divide training data and combine multiple classifiers to get a better performance of Chinese scene character recognition. Besides, facing the problem of data shortage, we propose a scene character synthesis method to gain enough training data. By applying the proposed training strategy, the average recognition accuracies of Random Forest (RF) and Support Vector Machine (SVM) have been improved by nearly 22% and 14%, respectively.

Original languageEnglish
Title of host publication2017 International Conference on Image and Vision Computing New Zealand, IVCNZ 2017
PublisherIEEE Computer Society
Pages1-5
Number of pages5
ISBN (Electronic)9781538642764
DOIs
Publication statusPublished - 2018 Jul 3
Event2017 International Conference on Image and Vision Computing New Zealand, IVCNZ 2017 - Christchurch, New Zealand
Duration: 2017 Dec 42017 Dec 6

Publication series

NameInternational Conference Image and Vision Computing New Zealand
Volume2017-December
ISSN (Print)2151-2191
ISSN (Electronic)2151-2205

Other

Other2017 International Conference on Image and Vision Computing New Zealand, IVCNZ 2017
CountryNew Zealand
CityChristchurch
Period17/12/417/12/6

Keywords

  • Chinese scene character
  • character recognition
  • dividing training
  • ensemble voting classifier
  • synthetic data

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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