Abstract:Due to the problematic scene background,irregular arrangement of text,and uncertain lighting conditions in natural scenes,text detection is difficult,and the traditional detection method is not ideal.In the study of the text segmentation detection method Progressive Scale Expansion Network (PSENet),an improved method for text detection in a natural scene is proposed.The improved model mainly uses the convolutional neural network to extract feature modules and performs segmentation detection on the text area through progressive scale expansion.The improvement points mainly uses a high-precision semantic segmentation network(RefineNet),optimizing the volume and network feature extraction modules,adding more residual connections and chain pooling,and improving the network's detection accuracy of the text area.Comparing the experimental results on the data set (ICDAR2015),the proposed improved algorithm is slightly more accurate than the previous algorithm and can better solve the problem of text conglutination.