Offline handwriting individual recognition system and method based on three-dimensional dynamic features

An offline handwriting individual recognition method based on three-dimensional dynamic features, applied to the offline handwriting individual recognition system of any one of claims 1-8, the method comprising the steps of:

• scanning, by the offline handwriting individual recognition system, the obtained suspicious handwriting to obtain a first white light image and a first three-dimensional image corresponding to the suspicious handwriting, and scanning a plurality of obtained sample handwriting respectively to obtain a plurality of second white light images and a plurality of second three-dimensional images corresponding to the plurality of sample handwriting;
• performing, by the offline handwriting individual recognition system, image pre-processing on the first white light image to obtain a first pre-processed image corresponding to the suspicious handwriting, and performing image pre-processing on each of the plurality of second white light images to obtain second pre-processed images corresponding to each of the plurality of sample handwriting;
• performing, by the offline handwriting individual recognition system, skeleton extraction on the first pre-processed image to obtain a first skeleton image corresponding to the suspicious handwriting, and performing skeleton extraction on each of the plurality of second pre-processed images to obtain second skeleton images corresponding to each of the plurality of sample handwriting;
• performing, by the offline handwriting individual recognition system, handwriting tracking according to the first skeleton image to obtain a first writing trajectory corresponding to the suspicious handwriting, and performing handwriting tracking according to each of the second skeleton images to obtain second writing trajectories corresponding to each of the plurality of sample handwriting;
• performing, by the offline handwriting individual recognition system, feature extraction on corresponding first white light image according to the first writing trajectory to obtain a first dynamic feature of each pixel in the first writing trajectory, and performing feature extraction on each corresponding second white light image according to the each of the second writing trajectories to obtain a second dynamic feature of each pixel in each of the second writing trajectories;
• extracting, by the offline handwriting individual recognition system, a first three-dimensional feature of each pixel in the first writing trajectory from a corresponding first three-dimensional image according to the first writing trajectory, and extracting a second three-dimensional feature of each pixel of the second writing trajectories from each of the corresponding second three-dimensional images according to the each of the second writing trajectories;
• processing, by the offline handwriting individual recognition system, to obtain a correlation coefficient between the suspicious handwriting and each of the plurality of sample handwriting, according to the first dynamic feature and the first three-dimensional feature corresponding to the suspicious handwriting, and the second dynamic feature and the second three-dimensional feature corresponding to the each of the plurality of sample handwriting;
• and
• processing, by the offline handwriting individual recognition system, the suspicious handwriting, according to a first probability density distribution data and a second probability density distribution data obtained in advance by using statistics and each of the correlation coefficients, to obtain an individual recognition result of the suspicious handwriting.