Efficient and Accurate Scene Text Detection with Low-Rank Approximation Network

Recently, regression-based methods, which predict parameter curves for localizing texts, are popular in scene text detection. However, these methods struggle to balance concise structure and fast post-processing, and the existing parameter curves are still not ideal for modeling arbitrary-shaped texts, leading to a challenge in balancing speed and accuracy. To tackle these challenges, we firstly propose a dual matching scheme for positive samples, which accelerates inference speed through sparse matching scheme and accelerates model convergence through dense matching scheme. Then, we propose a novel text contour representation method based on low-rank approximation by exploiting the shape correlation between different text contours, which is complete, compact, simplicity and robustness. Based on these designs, we implement an efficient and accurate arbitrary-shaped text detector, named LRANet. Extensive experiments are conducted on three challenging datasets, which demonstrate the accuracy and efficiency of our LRANet over state-of-the-art methods. The code will be released soon.