FRAME VIN CODE RECOGNITION METHOD BASED ON FASTER-RCNN AND PRIOR KNOWLEDGE

In order to increase the working efficiency of vehicle inspection office and overcome the problem of low accuracy of long character string recognition, we propose a VIN (Vehicle Identification Number) recognition model based on the existing deep convolutional neural network model, which combines Faster R-CNN as the backbone network with the prior knowledge of VIN image. According to the characteristic of VIN image, Faster R-CNN was selected for character level positioning and recognition. In order to solve the problem of missing characters in long character recognition, we used the coordinates of the characters before and after the missing position to locate the missing characters. We used the inception network to recognize the character regions obtained from the omission. By using prior knowledge flexibly, the accuracy of our method is 31.7 percentage points higher than the model of using Faster R-CNN only, the recognition rate reaches 64%, and that is also higher than the accuracy of prevailing OCR model in the recognition of character string that longer than 15.