The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
ASTER: An Attentional Scene Text Recognizer with Flexible Rectification
Abstract
A challenging aspect of scene text recognition is to handle text with distortions or irregular layout. In particular, perspective text and curved text are common in natural scenes and are difficult to recognize. In this work, we introduce ASTER, an end-to-end neural network model that comprises a rectification network and a recognition network. The rectification network adaptively transforms an input image into a new one, rectifying the text in it. It is powered by a flexible Thin-Plate Spline transformation which handles a variety of text irregularities and is trained without human annotations. The recognition network is an attentional sequence-to-sequence model that predicts a character sequence directly from the rectified image. The whole model is trained end to end, requiring only images and their groundtruth text. Through extensive experiments, we verify the effectiveness of the rectification and demonstrate the state-of-the-art recognition performance of ASTER. Furthermore, we demonstrate that ASTER is a powerful component in end-to-end recognition systems, for its ability to enhance the detector.
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Self-citationMask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
Lyu, P., Liao, M., Yao, C., Wu, W., & Bai, X. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_5 · Cited by 522
Self-citationShow, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
Li, H., Wang, P., Shen, C., & Zhang, G. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018610 · Cited by 432
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Metadata
- Authors
- Shi, B., Yang, M., Wang, X., Lyu, P., Yao, C., & Bai, X.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2019-09-01
- Volume
- 41
- Issue
- 9
- Pages
- 2035-2048
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2810983211
- Cited by
- 906
- References
- 68
- FWCI
- 36.64
- Type
- journal article
- Language
- en
- Open access
- closed
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