The improvements of computing performance make deep learning possible. As one of the important research directions in the field of computer vision, object detection has combined with deep learning methods and is widely used in all walks of life. Limited by the complexity of the network and the design of the detection algorithm, the speed and precision of the object detection becomes a trade-off. At present, the rapid development of electronic commerce has produced a large number of pictures containing the product parameters. The traditional method is hard to extract the information of the product parameters in the picture. This paper presents a method of combining deep learning detection algorithm with the traditional OCR technology to ensure the detection speed and at the same time greatly improve the accuracy of recognition. The paper focuses the following problems:The detection model, training for specific data, image preprocessing and character recognition. First, existing object detection algorithms are compared and their advantages and disadvantages are assessed. While the YOLO model is used to do the detection work, some improvements is proposed to overcome the shortcomings in the YOLO model. In addition, an object detection model is designed to detect the product parameters in images. Finally, tesseract is used to do the character recognition work. The experimental results show that the new system is efficient and effective in parameter recognition. At the end of this paper, the innovation and disadvantage of the presented method are discussed.