DBNetText Detection Algorithm Based on Edge Detection

In the booming e-commerce industry, precise text detection in product images is crucial for seamless operations.However, existing text detection algorithms face challenges due to the complex nature of e-commerce images.These images often combine intricate text with complex graphics and diverse product elements, all set against highly variable backgrounds.Artistic fonts, with their unique and often ornate designs, are especially difficult to detect accurately, leading to subpar performance in extracting product-related information.This inefficiency limits the development of intelligent e-commerce applications, which motivates our research.To address these challenges, we propose EIEM-DBNet, an edge-detection-based text detection algorithm.Its key innovation is the integration of the Edge Information Extraction Module (EIEM), which uses operators like Laplace, Sobel, and Canny to extract edge details from low-level feature maps.By emphasizing local edge features, EIEM-DBNet better distinguishes text from the complex background compared to traditional methods that rely on global features.After edge detection, a channel-weighting mechanism incorporates the extracted edge information into the model, enhancing its text detection accuracy.In terms of performance, EIEM-DBNet outperforms traditional DBNet models.In ablation experiments, it shows a 1.1% increase in recall, a 1.3% rise in accuracy, and a 1.2% improvement in F1-score.Compared to other advanced models, EIEM-DBNet achieves the highest recall rate in terms of F1-score, indicating its superior ability to balance precision and recall, thereby providing more accurate text detection in complex e-commerce image scenarios.