The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Focusing Attention: Towards Accurate Text Recognition in Natural Images
Open accessAbstract
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 automatically draw back the drifted attention. FAN consists of two major components: an attention network (AN) that is responsible for recognizing character targets as in the existing methods, and a focusing network (FN) that is responsible for adjusting attention by evaluating whether AN pays attention properly on the target areas in the images. Furthermore, different from the existing methods, we adopt a ResNet-based network to enrich deep representations of scene text images. Extensive experiments on various benchmarks, including the IIIT5k, SVT and ICDAR datasets, show that the FAN method substantially outperforms the existing methods.
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Self-citationText Recognition in the Wild: A Survey
Chen, X., Jin, L., Zhu, Y., Luo, C., & Wang, T. (2022) · ACM Computing Surveys · doi:10.1145/3440756 · Cited by 205
“The second category is segmentation-free methods [173], [161], [24], [163], [127].”
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Metadata
- Authors
- Cheng, Z., Bai, F., Xu, Y., Zheng, G., Pu, S., & Zhou, S.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 5086-5094
- OpenAlex
- W2750938222
- Cited by
- 528
- References
- 35
- FWCI
- 13.02
- Type
- conference paper
- Language
- en
- Open access
- green
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