Optimal Kernel for Real-Time Arbitrary-Shaped Text Detection

Recently, segmentation-based text detection methods develop rapidly, which achieve competitive accuracy and detection speed. However, these methods are hard to fit text instances accurately, which leads to the decrease of model performance. Meanwhile, the poor perception of the text center by the boundary pixels further affects the detection accuracy. We follow the issues and design an efficient framework for arbitrary-shaped text detection, which is constructed based on Optimal Kernel Representation (OKR) and Pixel Enhancement Module (PEM). Specifically, OKR is proposed to fit texts with optimal kernels. It erodes texts according to the corresponding geometric characteristics, which is simpler and more accurate compared with previous methods. PEM is used to enhance the perception of boundary pixels to the virtual character centers of text, thus improving the cohesion of the whole instance. Particularly, PEM only participates in the training process, which brings no extra computation costs to inference. Ablation experiments show the effectiveness of OKR and PEM. Comparisons on serveral benchmarks verify that our efficient detector is superior to the existing state-of-the-art (SOTA) methods.