Fast and accurate candidate reduction using the multiclass LDA for Japanese/Chinese character recognition

Ryosuke Odate, Hideaki Goto

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

3 Citations (Scopus)

Abstract

Acceleration of Optical Character Recognition (OCR) algorithms is quite important for developing real-time applications on mobile devices with limited computational performances. Multilingual scene text recognition is becoming more important for mobile and wearable devices. Since Japanese and Chinese have thousands of characters, a fast and accurate character recognition algorithm is required. We developed and proposed a tree-based clustering technique combined with Linear Discriminant Analysis (LDA), and it worked fine with ETL9B dataset consisting of Japanese handwritten characters. However, a significant performance degradation with HCL2000 Chinese handwritten character dataset was found. In this paper, we formalize the candidate reduction technique for the Nearest Neighbor (NN) problems, and propose an improved method that works fine with both Japanese and Chinese character sets. Experimental results show that our method is faster and more accurate than the existing acceleration techniques such as Approximate Nearest Neighbor (ANN) search and Locality Sensitive Hashing (LSH).

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages951-955
Number of pages5
ISBN (Electronic)9781479983391
DOIs
Publication statusPublished - 2015 Dec 9
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 2015 Sep 272015 Sep 30

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing, ICIP 2015
CountryCanada
CityQuebec City
Period15/9/2715/9/30

Keywords

  • Approximate Nearest Neighbor (ANN) search
  • Fast Nearest Neighbor search
  • Linear Discriminant Analysis (LDA)
  • multilingual OCR
  • real-time character recognition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Odate, R., & Goto, H. (2015). Fast and accurate candidate reduction using the multiclass LDA for Japanese/Chinese character recognition. In 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings (pp. 951-955). [7350940] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2015-December). IEEE Computer Society. https://doi.org/10.1109/ICIP.2015.7350940