Progressive Scale Expansion Network with Octave Convolution for Arbitrary Shape Scene Text Detection

Shi Yan, Wei Feng, Peng Zhao, Cheng-Lin Liu

Abstract: Scene text detection is a challenging problem due to the image cluttering and high variability of text shape. Many methods have been proposed for multi-oriented and arbitrary shape text detection, in which the storage and computation costs of deep neural networks are still concerns. In this paper, we first introduce Octave Convolution into scene text detection for enlarging the receptive fields and reducing the spatial redundancy of networks. Then we combine Octave Convolution with a state-of-the-art arbitrary shape text detector PSENet, which predicts different scale of kernels for each text instance. Experimental results on several benchmarks show that the proposed method can improve both detection performance and speed in detecting multi-oriented and arbitrary shape texts. Furthermore, our method achieves state-of-the-art performances on these benchmarks.