The present invention discloses a multi-directional text detection method based on a full convolution network. Firstly, the text block in a training data set is obtained, and a full convolution network model is trained; then the trained full convolution network model is used to detect the image in a test data set, and a text block is obtained; then the character component in the text block is extracted, and the candidate text bar position in the image is estimated according to the position information of the character component; finally, for the full convolution network model for the character component in the text block is trained, the trained model is used to detect a candidate text bar, the character component in the text bar is obtained, an unwanted text bar is filtered according to the character component, and the position of the text bar in the image is obtained. The text detection method has the advantages that the detection rate is high, and the influences of light, shelter, fuzzy, and other unfavorable factors can be overcome.