Parallel and Robust Text Rectifier for Scene Text Recognition
Bingcong Li (Ping An property&casualty insurance company of China.LTD.), xin tang (Ping An property&casualty insurance company of China.LTD.), Jun Wang (Ping An Technology (Shenzhen) Co. Ltd.), Liang Diao (Ping An property&casualty insurance company of China.LTD.), RUI FANG (Ping An Property & Casualty Insurance Company of China), Guotong Xie (Ping An Technology (Shenzhen) Co. Ltd.), Weifu Chen (Guangzhou Maritime University)*
The 33rd British Machine Vision Conference
Abstract
Scene text recognition (STR) is to recognize text appearing in images. Current state-of-the-art STR methods usually adopt a multi-stage framework which uses a rectifier to iteratively rectify errors from previous stage. However, the rectifiers of those models are not proficient in addressing the misalignment problem.To alleviate this problem, we proposed a novel network named Parallel and Robust Text Rectifier (PRTR), which consists of a bi-directional position attention initial decoder and a sequence of stacked Robust Visual Semantic Rectifiers (RVSRs). In essence, PRTR is creatively designed as a coarse-to-fine architecture that exploits a sequence of rectifiers for repeatedly refining the prediction in a stage-wise manner. RVSR is a core component in the proposed model which comprises two key modules, Dual-Path Semantic Alignment (DPSA) module and Visual-Linguistic Alignment (VLA). DPSA can rectify the linguistic misalignment issues via the global semantic features that are derived from the recognized characters as a whole, while VLA re-aligns the linguistic features with visual features by an attention model to avoid the overfitting of linguistic features. All parts of PRTR are non-autoregressive (parallel), and its RVSR re-aligns its output according to the linguistic features and the visual features, so it is robust to the mis-aligned error. Extensive experiments on mainstream benchmarks demonstrate that the proposed model can alleviate the misalignment problem to a large extent and outperformed state-of-the-art models.Video
Citation
@inproceedings{Li_2022_BMVC,
author = {Bingcong Li and xin tang and Jun Wang and Liang Diao and RUI FANG and Guotong Xie and Weifu Chen},
title = {Parallel and Robust Text Rectifier for Scene Text Recognition},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0770.pdf}
}
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