# Fast Localization and High Accuracy Recognition of Tire Surface Embossed Characters Based on CNN
## 1 Introduction  
The side pressure printing of the car tire provides information such as manufacturers, tire specifications, trademarks, production dates, etc. During tire storage, installation, testing, and other tasks, the tire character information needs to be read. At present, the identification and recording of tire side embossed characters are mostly performed manually, which is inefficient, labor intensive, and prone to problems such as omission and misremembering. With the rise of machine vision, visual inspection methods are widely used in industry. However, there are still difficulties in machine vision recognition of DOT embossed characters for automotive tires. Due to the low contrast between the characters and the tire background, as well as the tendency of the tire surface to have stains, wear, and other interference, manual detection is difficult, and it is difficult to accurately identify the characters with traditional OCR methods. With the rapid development of deep learning technology, the solution to this problem has seen a turnaround [1; 2; 3; 4].  
Reference [5] uses the image guidance filtering algorithm to process the images of the pressure printing characters and uses the drip segmentation algorithm to divide the characters. The classification method of Hog+SVM is classified using character classification and identification category comparison experiments. Reference [6] uses deep learning to identify the tire pressure printing character, uses the combination of Hoff transformation and partitional formula to detect the concentric circular area, and uses the Faster R-CNN algorithm for DOT identification positioning. The network structure recognizes characters. Reference [7] proposed a method of multi-step deep learning to achieve character information recognition. To improve the speed of operation, it also proposed that the end-to-end deep learning recognition framework be combined with the regional positioning algorithm performance. Reference [8] developed online recognition and classification systems based onmachine vision, obtained tire character segmentation images through template matching and edge detection algorithms, and finally implemented the classification recognition of characters through the minimum two-multiplication SVM. Reference [9] proposed a scale based on convolutional neural networks unchanged by convolutional neural networks to solve the problem of image scale changes and realize the positioning and recognition of characters. Reference [10] presented a deep convolutional network structure of its own design. This structure has a good improvement effect on character recognition through the non-linear operation of convolution kernel, lower sampling, and deep convolutional networks. Reference [11] proposed two lightweight neural networks with a hybrid residual and dense connection structure to improve the super-resolution performance. Reference [12] developed a lightweight convolutional neural network to realize automatic scratch detection for components in contact sliding, such as those in metal forming.  
This article aims to meet actual testing needs and identify the side-printed characters on the side of the tire. Based on the combination of traditional machine vision and a neural network, we first pre-process the collected images, divide them into single-character images, and use improved LENET-5 recognition of the digital characters, which improves the accuracy of character recognition. The CPU is completed without configured high-performance graphics cards, which greatly reduces the hardware cost, and significantly improves the training speed and recognition speed of the image.