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| Presentation | 2018-12-13 10:45 An attention-based encoder-decoder for recognizing Japanese historical document recognition Le Duc Anh (CODH), Mochihashi daichi (ISM), Masuda katsuya, Mima Hideki (UT)  PRMU2018-78 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Inspired by the recent successes of attention based encoder-decoder (AED) approach on image captioning, machine translation, we present an AED model as an end-to-end recognition system for recognizing Japanese historical document. The recognition system has two main modules: a dense convolution neural network for extracting multiscale features, and a Long Shor Term Memory (LSTM) decoder with attention model for generating target text. We can train the model end-to-end. The model requires only input text line images and corresponding output characters. Therefore, we don’t need the annotation in character level and save a lot of time for making annotations. The recognition system is trained by our annotated documents. We show the data imbalance problem in the current data and its effect on the performance of the recognition system through the experiments. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Japanese historical document / attention model / encoder-decoder approach / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 362, PRMU2018-78, pp. 19-22, Dec. 2018. | |
| Paper # | PRMU2018-78 | |
| Date of Issue | 2018-12-06 (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) | |
| Download PDF | PRMU2018-78 |
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| Conference Information | |
| Committee | PRMU |
| Conference Date | 2018-12-13 - 2018-12-14 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2018-12-PRMU |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | An attention-based encoder-decoder for recognizing Japanese historical document recognition |
| Sub Title (in English) | |
| Keyword(1) | Japanese historical document |
| Keyword(2) | attention model |
| Keyword(3) | encoder-decoder approach |
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| 1st Author's Name | Le Duc Anh |
| 1st Author's Affiliation | The Center for Open Data in the Humanities (CODH) |
| 2nd Author's Name | Mochihashi daichi |
| 2nd Author's Affiliation | The Institute of Statistical Mathematics (ISM) |
| 3rd Author's Name | Masuda katsuya |
| 3rd Author's Affiliation | The University of Tokyo (UT) |
| 4th Author's Name | Mima Hideki |
| 4th Author's Affiliation | The University of Tokyo (UT) |
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| Speaker | Author-1 |
| Date Time | 2018-12-13 10:45:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2018-78 |
| Volume (vol) | vol.118 |
| Number (no) | no.362 |
| Page | pp.19-22 |
| #Pages | 4 |
| Date of Issue | 2018-12-06 (PRMU) |
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