Binary tree-based precision-keeping clustering for very fast Japanese character recognition

Yohei Sobu, Hideaki Goto, Hirotomo Aso

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

3 Citations (Scopus)

Abstract

Real-time character recognition in video frames has been attracting great attention from developers since scene text recognition was recognized as a new field of Optical Character Recognition (OCR) applications. Some oriental languages such as Japanese and Chinese have thousands of characters, and the character recognition takes much longer time in general compared with European languages. Speed-up of character recognition is crucial to develop software for mobile devices such as Smart Phones. This paper proposes a binary tree-based clustering technique that can keep the precision as quite high as possible. The experimental results show that the character recognition using the proposed clustering technique is 8.3 times faster than the full linear matching at mere 0.22% precision drop. When the proposed method is combined with the Sequential Similarity Detection Algorithm (SSDA) and a PCA-based dimensionality reduction, we can achieve 36.2 times faster character matching at 0.29% precision drop.

Original languageEnglish
Title of host publicationIVCNZ 2010 - 25th International Conference of Image and Vision Computing New Zealand
DOIs
Publication statusPublished - 2010 Dec 1
Event25th International Conference of Image and Vision Computing New Zealand, IVCNZ 2010 - Queenstown, New Zealand
Duration: 2010 Nov 82010 Nov 9

Publication series

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

Other

Other25th International Conference of Image and Vision Computing New Zealand, IVCNZ 2010
CountryNew Zealand
CityQueenstown
Period10/11/810/11/9

Keywords

  • Japanese character recognition
  • character clustering
  • dimensionality reduction
  • fast matching algorithm
  • real-time character recognition

ASJC Scopus subject areas

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

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