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

Content Provider	IEEE Xplore Digital Library
Author	Odate, R. Goto, H.
Copyright Year	2015
Description	Author affiliation: Sch. of Eng., Tohoku Univ., Sendai, Japan (Odate, R.) \|\| Cyberscience Center, Tohoku Univ., Sendai, Japan (Goto, H.)
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).
Starting Page	951
Ending Page	955
File Size	594488
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7350940
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Character recognition Optical character recognition software Dictionaries Acceleration Performance evaluation Algorithm design and analysis Linear discriminant analysis multilingual OCR Fast Nearest Neighbor search Linear Discriminant Analysis (LDA) real-time character recognition Approximate Nearest Neighbor (ANN) search
Content Type	Text
Resource Type	Article

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