Abstract: Aiming at the difficulty of multi-directional and multi-angle text extraction and recognition in Chinese traffic guidance signs, a light-weight Chinese traffic guidance sign text extraction and recognition algorithm is proposed that integrates convolutional neural networks and traditional machine learning methods. Firstly, the YOLOv5l object detection network is lightly improved, and the YOLOv5t network is proposed to extract the text regions in the road signs. Secondly, an M-split algorithm combining the projection histogram method and the polynomial fitting method is proposed to segment the extracted text regions. Finally, the MobileNetV3 lightweight network is used to recognize the text. The proposed algorithm achieves a close-shot text recognition accuracy of 90.1% on the self-made TS-Detect dataset, the detection speed achieves 40 fps, and the size of the weight file is only 24.45 MB. The experimental results show that the algorithm is lightweight and accurate enough to complete the real-time Chinese guide sign text extraction and recognition tasks under complex shooting conditions.

Key words: traffic sign, text detection, polynomial fitting, YOLO, MobileNet ,