The invention discloses a
text detection method of a document image in a natural scene. Commonly used
Chinese characters are selected to make Chinese character pictures, a dataset 1 is formed, randomrevolving and
cropping operations are carried out on labeled document images, then a manner of Poisson
cloning is used to fuse different background images, and a dataset 2 is formed; the dataset 1 isadopted to carry out training of a text classification model on a VGG16 network, and after the model converges, obtained parameters are used to initialize a fully
convolutional neural network model, and the dataset 2 is used to
train the model; the trained fully-
convolutional neural network model is used to process the image, a classification situation of each pixel point is obtained according toa maximum probability method, and a text-non-text
binary image is formed; a method of connected regions is used to obtain text regions, the original image is binarized, and only text information in the text regions in the text-non-text region
binary image is extracted to obtain a text
binary image; the image is corrected through a maximum
variance method; and projection is carried out again on thecorrected image, and the text-non-text region binary image is refined.