关键词: 文字检测, 多尺度特征, 特征融合, 深度学习

Abstract: Aiming at the problem that existing text detection models cannot accurately detect text locations due to complex backgrounds and variable text region shapes in e-commerce images, an improved text detection model, named Iterative Self-selective Feature Fusion DBNet (iSFF-DBNet), is proposed. Firstly, after extracting features from the backbone network, an attention mechanism is introduced in the process of building a Feature Pyramid Network (FPN), and an Iterative Self-selective Feature Fusion (iSFF) module is proposed to enhance the feature extraction ability of the model. Finally, a bilinear upsampling module is introduced to improve the adaptive performance of the differentiable binaryization module. Experimental results show that compared to the standard DBNet model, the recall and F-score of the improved model are increased by 6.0% and 2.4%, respectively, in the text detection task of the ICPR MTWI 2018 web-scale image dataset. Compared with other text detection models, this model achieves a balance between accuracy and recall, and can detect text more accurately.

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