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.

提出了一种名为 LRANet 的文本检测器，该方法基于双重匹配方案用于正样本，利用低秩近似的文本轮廓表示方法来处理不规则形状文本，提高速度与准确性，并在三个数据集上展示了该方法的比较优越性。