The traditional approach of obtaining railway freight car numbers is manually recognizing car numbers, which has the drawbacks of low efficiency and error-prone. In this paper, we propose a small-sample-based method for railway freight car number recognition. The original sample data consists of character samples cropped from the target region of railway freight car numbers, with an average of 15 samples per character. Each type of character sample data is augmented to 500 images and undergoes image processing. Subsequently, an image stitching algorithm is employed to combine the processed character samples into a training set image. Using this training set, a character detection model is trained on the YOLOv5 algorithm. By incorporating spatial positional relationships between car number characters, a car number extraction algorithm is designed to output the final result. We test the proposed method, and our method achieved a car number recognition accuracy of 89.58%, demonstrating credible recognition performance.
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