The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition
Open accessAbstract
Image-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. A novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, is proposed. Compared with previous systems for scene text recognition, the proposed architecture possesses four distinctive properties: (1) It is end-to-end trainable, in contrast to most of the existing algorithms whose components are separately trained and tuned. (2) It naturally handles sequences in arbitrary lengths, involving no character segmentation or horizontal scale normalization. (3) It is not confined to any predefined lexicon and achieves remarkable performances in both lexicon-free and lexicon-based scene text recognition tasks. (4) It generates an effective yet much smaller model, which is more practical for real-world application scenarios. The experiments on standard benchmarks, including the IIIT-5K, Street View Text and ICDAR datasets, demonstrate the superiority of the proposed algorithm over the prior arts. Moreover, the proposed algorithm performs well in the task of image-based music score recognition, which evidently verifies the generality of it.
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Cites
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Cited by
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
MOTR: End-to-End Multiple-Object Tracking with Transformer
Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., & Wei, Y. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19812-0_38 · Cited by 752
FOTS: Fast Oriented Text Spotting with a Unified Network
Liu, X., Liang, D., Yan, S., Chen, D., Qiao, Y., & Yan, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00595 · Cited by 554
Focusing Attention: Towards Accurate Text Recognition in Natural Images
Cheng, Z., Bai, F., Xu, Y., Zheng, G., Pu, S., & Zhou, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.543 · Cited by 528
(2022) · doi:10.1007/978-3-031-19815-1_29 · Cited by 469
Metadata
- Authors
- Shi, B., Bai, X., & Yao, C.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2017-11-01
- Volume
- 39
- Issue
- 11
- Pages
- 2298-2304
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2194187530
- Cited by
- 3,172
- References
- 8
- FWCI
- 126.87
- Type
- journal article
- Language
- en
- Open access
- green
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