Calligraphy font text detection based on attention mechanism feature fusion and enhancement

A text detection algorithm based on attention-mechanism based feature fusion and enhancement for calligraphy fonts is proposed for the problem of diverse fonts, styles, forms, and complex structures in calligraphy works, and the network performance is improved by building an attention-mechanism based feature fusion module (AFFM) and an attention-mechanism based feature enhancement module (AFEM). First, according to the characteristics of calligraphic text, the AFFM module adds a feature pyramid FPN to combine global information with remote contextual information and improve the connection between each feature channel to improve the accuracy of text detection; second, the AFEM module improves the prediction accuracy of the model in predicting text pixels by enhancing the characterization ability of feature information after fusion of feature maps at different scales. The experimental results on the self-built text detection dataset of calligraphy works show that the precision, recall, and overall evaluation metrics F1 reach 94.29%, 94.07%, and 94.18%, respectively, proving the effectiveness of the method on the text detection task of calligraphy fonts. Meanwhile, on the public dataset ICDAR2015, its precision, recall, and overall evaluation metrics F1 reached 89.09%, 76.76%, and 82.47%, respectively, proving that the model has good generalization properties.