In recent years, many scene text detection methods based on generic object detection framework have been proposed. These methods usually predict the entire bounding box of the text directly, while it is difficult for them to detect long text effectively due to the limit of receptive field. To solve such problem, a scene text detection method based on short edge vertices regression network was proposed. The method divides the text region into three kinds of regions, namely regions near two short edges and middle region, where separate text regions are firstly predicted and then combined, whereas the entire bounding box of the text is not predicted directly. Specifically, three kinds of regions were segmented on a residual network combined with multi-scale features, and two vertices of a short edge were predicted at each pixel in the region near the short edge. Then the regions near the two short edges were combined on the basis of the adjacent relationship between middle region and short edge regions in the post process, and vertices of short edges predicted by the two regions near short edges were combined to generate complete and accurate detection results. Finally, experiments were performed on a long text detection dataset and several public scene text detection datasets such as MSRA-TD 500, ICDAR 2015, and ICDAR 2013. The proposed method outperformed most of existing methods in accuracy and speed. Experimental results demonstrate that the method has advantages in text detection, especially for long text.