Abstract
With the development of the economy and the surge in car ownership, the sale of used cars has been welcomed by more and more people, and the information of the vehicle condition is the focus information of them. The frame number is a unique number used in the vehicle, and by identifying it can quickly find out the vehicle models and manufacturers. The traditional character recognition method has the problem of complex feature extraction, and the convolutional neural network has unique advantages in processing two-dimensional images. This paper analyzed the key techniques of convolutional neural networks compared with traditional neural networks, and proposed improved methods for key technologies, thus increasing the recognition of characters and applying them to the recognition of frame number characters.
Highlights
In the 2018 Boao Forum for Asia (BFA) annual meeting, auto industry has been mentioned twice during the speech given by China Chairmen, Mr Xi Jinping
The traditional character recognition method has the problem of complex feature extraction, and the convolutional neural network has unique advantages in processing two-dimensional images
This paper analyzed the key techniques of convolutional neural networks compared with traditional neural networks, and proposed improved methods for key technologies, increasing the recognition of characters and applying them to the recognition of frame number characters
Summary
In the 2018 Boao Forum for Asia (BFA) annual meeting, auto industry has been mentioned twice during the speech given by China Chairmen, Mr Xi Jinping. With the development of the auto car science and the rising oil price, the second hand market is rapidly developing. During the procurement of auto cars and with the applying of frame number identification, it will be quick to match the data of service provider and make all the information of the said cars clear, including accidents and illegal driving. The buyer could know the statues of the cars anytime and anywhere after the auto vehicle database is established [1]. With the addition of Feature Learning to the multi-layer neural network and the unique advantages of image recognition, the Convolutional Neural Network (CNN) technology is widely studied in the field of computer vision, model matching and pattern recognition. By the CNN character recognition, has been the extract features processing mechanisms in the human visual system simulated to extract underlying image feature information, and the end-to-end feature extraction has been utilized to enhance the generalization ability and avoid the inaccuracy due to the image morphing
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