Scene text detection methods based on segmentation have been widely employed in the field of text detection, which refers to locate text region and mark using text box in the scene images. While, for the diverse characteristics of text in medical scenes such as complex backgrounds, text with slanted, curved, dense, multi-directional and stamp interference text, existing detection methods still struggle to achieve ideal results. In light of this, this paper presents a refined model Bidirectional-Weighted Effective Multi-Level DBNet (BEM-DBNet) based on DBNet. The architecture consists of Global and Local Attention Feature Fusion(GLA), Bi-Weighted Feature Augmentation(BFA) and Multi-level Feature Fusion(MFF). The GLA is responsible for integrating attention mechanisms at both global and local levels. It enables the model to focus on the overall structure of the text while simultaneously capturing fine-grained details. This dual-focus approach enhances the model’s ability to detect deformed text in complex backgrounds more effectively and compensates for the lack of small-scale text. BFA is a cascaded effective weighted bidirectional feature pyramid network. It enables the network to assess the importance of different input features, leading to more refined feature selection and fusion. MFF is a feature fusion structure that uses enhanced features across scales, facilitating information sharing at different spatial levels. Additionally, a differentiable binarization post-processing module is used, enabling the segmentation network to adjust the binarization threshold dynamically. Validation on multiple datasets shows the effectiveness and feasibility of our method. Codes are available at: https://github.com/wennuan/BEM-DBNet