APSENet: A Text Line Detection Method of Manchu Archives Based on Instance Segmentation Network

Text line detection is an important link in the digitization of Manchu archives, but there are few relevant studies at present, especially the problem that long text is difficult to detect. This paper proposes a text line detection method of Manchu archives called APSENet based on the idea of PSENet image example segmentation model. This method uses ResNet network to extract the text line features of Manchu archives. By introducing the progressive scale expansion algorithm for the segmentation mask of post-processing network output, it can effectively solve the problem that long text is difficult to detect. By introducing the feature channel attention mechanism, it can solve the problem of large text box margin caused by irrelevant background interference. Experimental results show that the algorithm can achieve good detection results