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- research-articleOctober 2023
Fine-grained Pseudo Labels for Scene Text Recognition
MM '23: Proceedings of the 31st ACM International Conference on MultimediaPages 5786–5795https://doi.org/10.1145/3581783.3611791Pseudo-Labeling based semi-supervised learning has shown promising advantages in Scene Text Recognition (STR). Most of them usually use a pre-trained model to generate sequence-level pseudo labels for text images and then re-train the model. Recently, ...
- research-articleOctober 2019
A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning
- Pengfei Wang,
- Chengquan Zhang,
- Fei Qi,
- Zuming Huang,
- Mengyi En,
- Junyu Han,
- Jingtuo Liu,
- Errui Ding,
- Guangming Shi
MM '19: Proceedings of the 27th ACM International Conference on MultimediaPages 1277–1285https://doi.org/10.1145/3343031.3350988Detecting scene text of arbitrary shapes has been a challenging task over the past years. In this paper, we propose a novel segmentation-based text detector, namely SAST, which employs a context attended multi-task learning framework based on a Fully ...
- ArticleDecember 2015
Extraction of Virtual Baselines from Distorted Document Images Using Curvilinear Projection
ICCV '15: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV)Pages 3925–3933https://doi.org/10.1109/ICCV.2015.447The baselines of a document page are a set of virtual horizontal and parallel lines, to which the printed contents of document, e.g., text lines, tables or inserted photos, are aligned. Accurate baseline extraction is of great importance in the ...
