Focusing Attention: Towards Accurate Text Recognition in Natural Images
Fudan University · Shanghai Jiao Tong University
Abstract
Scene text recognition has been a hot research topic in computer vision due to its various applications. The state of the art is the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. However, we observe that existing attention-based methods perform poorly on complicated and/or low-quality images. One major reason is that existing methods cannot get accurate alignments between feature areas and targets for such images. We call this phenomenon “attention drift”. To tackle this problem, in this paper we propose the FAN (the abbreviation of Focusing Attention Network) method that employs a focusing attention mechanism to…
Citation impact
- FWCI
- 18.76
- Percentile
- 100%
- References
- 50
Authors
6Topics & keywords
- Computer science
- Artificial intelligence
- Feature (linguistics)
- Attention network
- Encoder
- Character recognition
- Feature extraction
- Pattern recognition (psychology)