Scene Text Recognition via Dual-path Network with Shape-driven Attention Alignment
Abstract
References
Index Terms
- Scene Text Recognition via Dual-path Network with Shape-driven Attention Alignment
Recommendations
Towards Accurate Alignment and Sufficient Context in Scene Text Recognition
Neural Information ProcessingAbstractEncoder-decoder framework has recently become cutting-edge in scene text recognition (STR), where most decoder networks consist of two parts: an attention model to align visual features from the encoder for each character, and a linear or LSTM-...
Thai Scene Text Recognition with Character Combination
Pattern Recognition and Computer VisionAbstractIn recent years, scene text recognition(STR) that recognizing character sequences in natural images is in great demand beyond various fields. However, most STR studies only focus on popular scripts like Chinese or English, too little attention has ...
Deep neural network with attention model for scene text recognition
The authors present a deep neural network (DNN) with attention model for scene text recognition. The proposed model does not require any segmentation of the input text image. The framework is inspired by the attention model presented recently for speech ...
Comments
Information & Contributors
Information
Published In
- Editor:
- Abdulmotaleb El Saddik
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- National Natural Science Foundation of China
- Jiangsu Science and Technology Programme (Natural Science Foundation of Jiangsu Province)
- European Union’s Horizon 2020 research and innovation programme
- UK EPSRC under projects
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 285Total Downloads
- Downloads (Last 12 months)285
- Downloads (Last 6 weeks)43
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in