  Recently, regression-based methods, which predict parameterized text shapes
for text localization, have gained popularity in scene text detection. However,
the existing parameterized text shape methods still have limitations in
modeling arbitrary-shaped texts due to ignoring the utilization of
text-specific shape information. Moreover, the time consumption of the entire
pipeline has been largely overlooked, leading to a suboptimal overall inference
speed. To address these issues, we first propose a novel parameterized text
shape method based on low-rank approximation. Unlike other shape representation
methods that employ data-irrelevant parameterization, our approach utilizes
singular value decomposition and reconstructs the text shape using a few
eigenvectors learned from labeled text contours. By exploring the shape
correlation among different text contours, our method achieves consistency,
compactness, simplicity, and robustness in shape representation. Next, we
propose a dual assignment scheme for speed acceleration. It adopts a sparse
assignment branch to accelerate the inference speed, and meanwhile, provides
ample supervised signals for training through a dense assignment branch.
Building upon these designs, we implement an accurate and efficient
arbitrary-shaped text detector named LRANet. Extensive experiments are
conducted on several challenging benchmarks, demonstrating the superior
accuracy and efficiency of LRANet compared to state-of-the-art methods. Code is
available at: \url{https://github.com/ychensu/LRANet.git}
