Text Detection Based on Bidirectional Feature Fusion and SA Attention Mechanism
Bowen Liu, Jingxuan Jin · 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) · 2022
Most of the scene text detection methods can not balance accuracy and speed, so these text detection methods are difficult to apply to the scenarios with real-time requirements. We propose a text detection network that attempts to improve text detection’s accuracy while guaranteeing the speed of text detection. Firstly, we use ResNet with SA attention mechanism as a backbone network for extracting image features. Then, different feature layers are fused by a bidirectional feature fusion mechanism, and a bilinear interpolation method is adopted for upsampling to reduce the feature loss in the feature fusion process. Finally, the post-processing part uses a differentiable binarization module to ensure the generalization and real-time performance of the model. The model has good performance on public dataset, achieving 41 of FPS and 83.2% of F-measure on the ICDAR2015, which verify the model has both good real-time performance and high accuracy.