Multi-level Feature Enhancement Method for Medical Text Detection

In recent years, Segmentation-based text detection methods have been widely applied in the field of text detection. However, when it comes to tasks involving text-dense, extreme aspect ratios, and multi-oriented text, the current detection methods still fail to achieve satisfactory performance. In this paper, we propose an efficient and accurate text detection system that incorporates an efficient segmentation module and a learnable post-processing method. More specifically, the segmentation head consists of an Efficient Feature Enhancement Module (EFEM) and a Multi-Scale Feature Fusion Module(MSFM). The EFEM is a cascaded U-shaped module that incorporates spatial attention mechanism to introduce multi-level information for improved segmentation performance. MSFM integrates features from the EFEM at different depths and scales to generate the final features for segmentation. Moreover, a post-processing module employing a differentiable binarization approach is utilized, allowing the segmentation network to adaptively set the binarization threshold. The proposed model demonstrates excellent performance on medical text image datasets. Multiple benchmark experiments further validate the superior performance of the proposed method. Code is available at: https://github.com/csworkcode/EFDBNet .