The invention provides a character segmentation and recognition method based on a CTC deep neural network. The method comprises the following steps: a1, extracting features of an input image by usinga CNN; a2, carrying out the
CELL segmentation on the features extracted in a1, fixing the height and width of
CELL, and determining the number of
CELL by the length of the image; a3, directly segmenting and classifying each CELL of the determined features, and outputting segmentation signals; a4, calculating the loss between the real segmentation
signal and the segmentation
signal output by the model by using CTCLOSS, feeding back the loss condition and training the whole model; a5, segmenting the text by using the segmentation
signal output in the step a3, carrying out CNN + softmax classification identification on a
single character, mapping a real segmentation signal from the annotated text, and automatically solving the
text alignment problem by using the CTCLOSS. According to the invention, the OCR recognition speed is improved, and the recognition optimization is targeted after the characters are
cut into single characters, so the final precision is improved; a recognition framework is improved, and a recognition process is separated into character segmentation and
single character recognition, so optimization can be separately carried out in a targeted manner.