DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition-Reference-Cited by-同舟云学术

DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition

Published:2023 Issue: Volume: Page:1-17
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Coquenet Denis¹^ORCID,Chatelain Clement²^ORCID,Paquet Thierry¹^ORCID

Affiliation:

1. LITIS EA 4108, University of Rouen Normandy and Normandie Université, France

2. LITIS EA 4108, INSA Rouen Normandy and Normandie Université, France

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/4359286/10013687.pdf?arnumber=10013687

Reference52 articles.

1. Chargrid-OCR: End-to-end trainable optical character recognition through semantic segmentation and object detection;reisswig,2019

2. Chargrid: Towards Understanding 2D Documents

3. BERT: Pre-training of deep bidirectional transformers for language understanding;devlin;Proc Conf North Amer Chapter Assoc Comput Linguistics Hum Lang Technol,2019

4. BERTgrid: Contextualized embedding for 2D document representation and understanding;denk,2019

5. Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition?

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