Abstract: In order to solve the problems that the existing method of pointer meter recognition depended on the validity of preprocessing and the lack of generalization ability,a hybrid method of automatic recognition on pointer meters combining deep convolutional neural networks with virtual samples is proposed.The deep convolutional neural networks are used to adaptively extract the key features of the instrument image to avoid the interference of irrelevant information.The prior knowledge is used to construct the virtual sample generation model of the pointer meter to solve the small sample problem faced by the deep convolutional neural networks.Simulation data,experimental data and practical application were applied to validate the method.The results show that the method achieves better effect and good robustness with its application on different instruments under various scenes.