Paper Info
Title
Sequence-To-Sequence Domain Adaptation Network For Robust Text Image Recognition
Abstract
Domain adaptation has shown promising advances for alleviating domain shift problem. However, recent visual domain adaptation works usually focus on non-sequential object recognition with a global coarse alignment, which is inadequate to transfer effective knowledge for sequence-like text images with variable-length fine-grained character information. In this paper, we develop a Sequence-to-Sequence Domain Adaptation Network (SSDAN) for robust text image recognition, which could exploit unsupervised sequence data by an attention-based sequence encoder-decoder network. In the SSDAN, a gated attention similarity (GAS) unit is introduced to adaptively focus on aligning the distribution of the source and target sequence data in an attended character-level feature space rather than a global coarse alignment. Extensive text recognition experiments show the SSDAN could efficiently transfer sequence knowledge and validate the promising power of the proposed model towards real world applications in various recognition scenarios, including the natural scene text, handwritten text and even mathematical expression recognition.
Year
DOI
Venue
2019
10.1109/CVPR.2019.00285
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
ISSN
Computer vision,Pattern recognition,Domain adaptation,Computer science,Artificial intelligence
Conference
1063-6919
Citations 
PageRank 
References 
7
0.40
0
Authors
6
Name
Order
Citations
PageRank
Ya-Ping Zhang1114.66
Shuai Nie2408.30
Wenju Liu321439.32
Xing Xu476462.73
Dongxiang Zhang574343.89
Heng Tao Shen66020267.19