The Network Design of License Plate Recognition Based on the Convolutional Neural Network

Handwritten image recognition based on CNN algorithm

“…Convolutional neural network has a high fault tolerance rate, can handle multiple tasks at the same time, and has a certain self-learning ability. It can effectively deal with the common sample defects, ambiguities and distortions in handwritten digits [10], and the recognition system established based on convolutional neural network has a fast running speed and high adaptive ability. The reason why convolutional neural network technology can have such superior performance is mainly because it can perceive the sample features at multiple levels by means of structure recombination and weight reduction, which can reduce the relatively complex image feature separation work in the early stage of conventional digital recognition.…”

A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry

“…With the successful development and application of deep learning in the field of computer vision, such as face recognition and detection, relevant advanced face recognition algorithms are also widely applied to the research of Automatic License Plate Recognition [4][5][6][7][8]. However, the license plate recognition technology still has many challenges in reality, such as the pixel level of the camera, the effects of light and shadow during the day and at night, different weather conditions, different shooting angles, and even possible reflections or stains on the license plates, all of which are complex variables so that the license plate recognition system is prone to recognition errors or failures.…”