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Paper Abstract and Keywords
Presentation 2022-03-11 11:00
Toward "Virtually" 100%; Quality Assurance Framework for Document Recognition
Hiroshi Tanaka (Fujitsu) PRMU2021-79
Abstract (in Japanese) (See Japanese page) 
(in English) With the development of DL (deep learning) technology, the recognition accuracy of OCR has improved dramatically. At the same time, however, it is pointed out that DL-based software cannot be used with confidence because it cannot be guaranteed to work properly due to inevitable errors. This has been a longstanding issue in pattern recognition, but with the recent AI boom, the problem has become more apparent in the form of "quality assurance of AI" and "explainability of AI. In this paper, I will give an overview of the quality assurance issues in AI software development, which have been discussed especially actively in the last couple of years, and propose a quality assurance framework with a particular focus on OCR.
Keyword (in Japanese) (See Japanese page) 
(in English) AI / OCR / Deep Learning / Quality Assurance / Guideline / Framework / Evaluation /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 427, PRMU2021-79, pp. 121-126, March 2022.
Paper # PRMU2021-79 
Date of Issue 2022-03-03 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee PRMU IPSJ-CVIM  
Conference Date 2022-03-10 - 2022-03-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Differentiable rendering 
Paper Information
Registration To PRMU 
Conference Code 2022-03-PRMU-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Toward "Virtually" 100%; Quality Assurance Framework for Document Recognition 
Sub Title (in English)  
Keyword(1) AI  
Keyword(2) OCR  
Keyword(3) Deep Learning  
Keyword(4) Quality Assurance  
Keyword(5) Guideline  
Keyword(6) Framework  
Keyword(7) Evaluation  
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1st Author's Name Hiroshi Tanaka  
1st Author's Affiliation Fujitsu Limited (Fujitsu)
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Speaker Author-1 
Date Time 2022-03-11 11:00:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2021-79 
Volume (vol) vol.121 
Number (no) no.427 
Page pp.121-126 
#Pages
Date of Issue 2022-03-03 (PRMU) 


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