Multi-scale Information Fusion Combined with Residual Attention for Text Detection

Driven by deep learning and neural networks, text detection technology has made further developments. Due to the complexity and diversity of scene text, detecting text of arbitrary shapes has become a challenging task. Previous segmentation-based text detection methods can hardly solve the problem of missed detection in complexity scene text detection. In this paper, we propose a text detection model that combines residual attention with a multi-scale information fusion structure to effectively capture text information in natural scenes and avoid text omission. Specifically, the multi-scale information fusion structure extracts text features from different levels to achieve better text localisation and facilitate the fusion of text information. At the same time, residual attention is combined with features from high-resolution images to enhance the contextual information of the text and avoid text omission. Finally, text instances are obtained by a binarisation method. The proposed model is very helpful for text detection in complex scenes. Experiments conducted on three public benchmark datasets show that the model achieves state-of-the-art performance.