Recognition of Casting Embossed Convex and Concave Characters Based on YOLO v5 for Different Distribution Conditions

Traditional casting character recognition algorithms need to select appropriate position features for different scenes in the character location step, so it is difficult to realize the recognition task of casting embossed concave and convex characters in the different distribution in complex scenes. In this letter, a recognition method of casting embossed characters based on YOLO v5 is proposed. The fast and reliable depth learning algorithm YOLO v5 is used to automatically extract the image features and realize the recognition of casting embossed characters (including numbers and letters) Recognition. The experimental results show that the accuracy of the network model for steel seal character recognition is higher than traditional computer vision algorithms, the average processing time of the algorithm is quickly, and the weight file volume is small, which meets the accuracy and efficiency requirements of engineering application.