TRIE: End-to-End Text Reading and Information Extraction for Document Understanding-Reference-Cited by-同舟云学术

TRIE: End-to-End Text Reading and Information Extraction for Document Understanding

Published:2020-10-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM International Conference on Multimedia
language:
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Author:

Zhang Peng¹,Xu Yunlu¹,Cheng Zhanzhan²,Pu Shiliang¹,Lu Jing¹,Qiao Liang¹,Niu Yi¹,Wu Fei³

Affiliation:

1. Hikvision Research Institute, Hangzhou, China

2. Zhejiang University & Hikvision Research Institute, Hangzhou, China

3. Zhejiang University, Hangzhou, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394171.3413900

Reference62 articles.

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2. Michal Busta Lukas Neumann and Jiri Matas. 2017. Deep TextSpotter: An End-to-End Trainable Scene Text Localization and Recognition Framework. In ICCV. 2223--2231. Michal Busta Lukas Neumann and Jiri Matas. 2017. Deep TextSpotter: An End-to-End Trainable Scene Text Localization and Recognition Framework. In ICCV. 2223--2231.

3. Manuel Carbonell Alicia Forné s Mauricio Villegas and Josep Lladó s. 2019. TreyNet: A Neural Model for Text Localization Transcription and Named Entity Recognition in Full Pages. arXiv preprint arXiv:1912.10016 (2019). Manuel Carbonell Alicia Forné s Mauricio Villegas and Josep Lladó s. 2019. TreyNet: A Neural Model for Text Localization Transcription and Named Entity Recognition in Full Pages. arXiv preprint arXiv:1912.10016 (2019).

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