Text Detection Based on Feature Fusion and Attention Enhancement in Natural Scenes

Text Spotting for natural scenes is different from traditional Optical Character Recognition (OCR) tasks in that the text instances it detects usually lack a fixed format, have a messy layout, and are susceptible to background factors such as light and occlusion. Especially in the case of dense distribution of text instances, small text instances are easily missed; while in complex environments, irregular or arbitrarily shaped text instances further increase the difficulty of detection. To address the above challenges, this paper proposes an improved method. The method compensates for the lack of small text omission detection by sampling and fusing the underlying feature maps, and introduces a feature enhancement method with cross-dimensional interaction in the attention mechanism to improve the detection performance of irregular text instances. Experimental results show that the proposed method can significantly improve the model's text detection performance in natural scenes.