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.

翻译：近年来，基于回归的方法通过预测参数曲线来定位文本，在场景文本检测中备受关注。然而，这些方法难以兼顾简洁结构与快速后处理，且现有参数曲线在建模任意形状文本时仍不理想，导致速度与精度难以平衡。针对上述挑战，我们首先提出一种针对正样本的双重匹配方案，该方案通过稀疏匹配加速推理，并通过密集匹配加速模型收敛。其次，我们利用不同文本轮廓间的形状相关性，提出一种基于低秩近似的全新文本轮廓表示方法，该方法兼具完整性、紧凑性、简洁性与鲁棒性。基于这些设计，我们实现了一个高效精准的任意形状文本检测器LRANet。在三个具有挑战性的数据集上进行的广泛实验表明，我们的LRANet在准确率和效率上均优于现有最先进方法。代码即将开源。