In order to further improve the detection accuracy of the text detector based on convolutional neural networks, first, feature extraction network with split-attention mechanism is used to replace the backbone network of the original algorithm, such as residual network, to promote information exchange between channels and maximize the activation of text features. Second, based on the original feature pyramid network, a bottom-up path is added to reduce the loss of text feature information. Experimental results show that the average accuracy of the algorithm is 78.7% and 79.0% on CTW1500 and Total-Text curve data sets, and 82.7% and 79.3% in multi-directional and multi-language data sets, respectively, which is better than other algorithms.