SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition

Scene text recognition is a hot research topic in computer vision. Recently, many recognition methods based on the encoder-decoder framework have been proposed, and they can handle scene texts of perspective distortion and curve shape. Nevertheless, they still face lots of challenges like image blur...

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Veröffentlicht in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 13525 - 13534
Hauptverfasser: Qiao, Zhi, Zhou, Yu, Yang, Dongbao, Zhou, Yucan, Wang, Weiping
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2020
Schlagworte:
Decoding
Feature extraction
Image recognition
Semantics
Task analysis
Text recognition
Visualization
ISSN:1063-6919
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Zusammenfassung:Scene text recognition is a hot research topic in computer vision. Recently, many recognition methods based on the encoder-decoder framework have been proposed, and they can handle scene texts of perspective distortion and curve shape. Nevertheless, they still face lots of challenges like image blur, uneven illumination, and incomplete characters. We argue that most encoder-decoder methods are based on local visual features without explicit global semantic information. In this work, we propose a semantics enhanced encoder-decoder framework to robustly recognize low-quality scene texts. The semantic information is used both in the encoder module for supervision and in the decoder module for initializing. In particular, the state-of-the-art ASTER method is integrated into the proposed framework as an exemplar. Extensive experiments demonstrate that the proposed framework is more robust for low-quality text images, and achieves state-of-the-art results on several benchmark datasets. The source code will be available.
ISSN:1063-6919
DOI:10.1109/CVPR42600.2020.01354