Abstract: The present disclosure provides a license plate recognition method, a device thereof, and a user equipment. In the method, a vehicle image is acquired and input to a vehicle searching convolutional neural network (CNN) to search whether there is a vehicle. Then, a license plate image is acquired and input to a license plate searching CNN to search whether there is a license plate. And the license plate image is input to a tensor neural network to detect abnormal or not. Then, a recognition image is acquired and input to judge an area number, letters, and numbers. And an area number image, a letter image, and a number image are output and input to their corresponding recognition CNNs respectively to recognize an area number, letters, and numbers and output respectively. Ultimately, a license plate number recognition result in a form of “area number letter number” are output.

Abstract: A method for processing tensor data for pattern recognition and a computer device are provided. The method includes: constructing a decision function by the optimal projection tensor W which has been rank-one decomposed together with the offset scalar b, and inputting to-be-predicted tensor data which has been rank-one decomposed into the decision function for prediction.

Abstract: The present disclosure provides a license plate recognition method, a device thereof, and a user equipment. In the method, a vehicle image is acquired and input to a vehicle searching convolutional neural network (CNN) to search whether there is a vehicle. Then, a license plate image is acquired and input to a license plate searching CNN to search whether there is a license plate. And the license plate image is input to a tensor neural network to detect abnormal or not. Then, a recognition image is acquired and input to judge an area number, letters, and numbers. And an area number image, a letter image, and a number image are output and input to their corresponding recognition CNNs respectively to recognize an area number, letters, and numbers and output respectively. Ultimately, a license plate number recognition result in a form of “area number letter number” are output.

Abstract: Optimization method and system for supervised learning under tensor mode is provided; wherein the method includes: receiving an input training tensor data set; introducing a within class scatter matrix into an objective function such that between class distance is maximized, at the same time, within class distance is minimized by the objective function; constructing an optimal frame of the objective function of an optimal projection tensor machine OPSTM subproblem; constructing an optimal frame of an objective function of an OPSTM problem; solving the revised dual problem and outputting alagrangian optimal combination and an offset scalar b; calculating a projection tensor W*; calculating a optimal projection tensor W; by the W together with the b, constructing a decision function; inputting to-be-predicted tensor data which has been rank-one decomposed into the decision function for prediction.