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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PRMU2021-79 |
Conference Information |
Committee |
PRMU IPSJ-CVIM |
Conference Date |
2022-03-10 - 2022-03-11 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
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(See Japanese page) |
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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) |
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AI |
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OCR |
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Deep Learning |
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Quality Assurance |
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Framework |
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Evaluation |
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Hiroshi Tanaka |
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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 |
6 |
Date of Issue |
2022-03-03 (PRMU) |
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