Abstract: In view of the issue that current mainstream segmentation-based text detection methods is difficult to achieve high detection speed due to complex post-processing to ensure detection accuracy,a scene text detec-tion method is proposed which applies pyramid attention network and position attention module.First,it adopts pyramid attention network to perform feature extraction and semantic segmentation.Meanwhile,it adopts po-sition attention module in high-level features,which strengthens the weights of similar objects in the image to enhance the effect of text detection.Finally,it adopts a simple and effective post-processing algorithm to in-crease detection speed under the premise of high detection accuracy.Experimental results show that in To-tal-text datasets,using light-weight backbone network,the method has great advantages on detection speed,and while using deeper backbone network,the method achieves the state of the art result and has a 2.0%lead on detection accuracy.