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Scene Text Detection based on Dual-branch Multi-resolution Feature-aware Enhancement Network

Arbitrary shape scene text detection becomes a challenge task due to its background complexity and shape diversity. In this paper, we propose a dual-branch multi-resolution feature-aware enhancement network (DMFE), the lower branch constructs multi-resolution features through a bidirectional feature pyramid network with weights, and the upper branch enhances the perception of multi-scale text at each level through parallel pooling modules with receptive field enhancement. The global and local co-action will integrate high-level semantic information and low-level location information, so as to generate high-quality feature map. Extensive experiments on ICDAR2015, CTW1500 and Total-Text datasets show that the proposed method effectively improves the detection performance of natural scene text.