In daily life,there are rich text information,the extraction of such information can greatly improve people′s quality of life.However,there are va rious forms of text information expression and different text shapes in natural scenes ,which result in false detection and inaccurate location of text regions.In order to s olve these problems,this paper proposes a text detection method with self-calibration and attention mechanism.Firstly,the self-calibrated convolution (SConv) and efficient cha nnel attention (ECA) are embedded in the backbone of ResNet50 to correct the interference o f irrelevant global information and concentrate on the text area to extract more a bundant semantic information Secondly,coordinated attention (CA) is added after feature fusi on to correct the position deviation of feature map in different scale.Finally,sever al text instances of different scales are predicted by the modified feature map,and the final detected text instances are obtained by using the progressive scale expansion algorithm.The experimental results show that the comprehensive index F-measure is increased by 1.0%,5.2% and 5.4% respectively compared with the unmodified ResNet50 on the arbitrary direction data set ICDAR2015 and the curved text data set Total-Text and SCUT-CTW1500.It is proved that this method has good detection ability.