Aiming at the challenge of recognition difficulties has been rising for electricity meter information including readings and labels because electricity meter image taken is susceptible to light, stains, and shooting angles, this paper proposes an information recognition method for electricity meter based on template matching and deep neural network. Firstly, the type of electricity meter including high-voltage and low-voltage energy meter is determined by using SIFT features to match the meter image with template images. Then, the screen area of the meter is accurately extracted by using edge information and Hough transformation. Furthermore, the on-screen readings and labels area of the meter are obtained respectively with aid of matching calibration information of the standard template. On this basis, segmentation tasks of the readings area and labels area are finished by utilizing equal space segmentation method and binary model respectively. Finally, the readings of the meter are recognized by running a digital recognition network. The proposed method makes full use of the template calibrated information in advance and solves the readings detection problem under complex conditions by a simple and effective equidistant segmentation process, and changes complex text recognition to a simple and efficient binary detection task. Therefore, it has better robustness to recognize the reading and text information of electricity meter. Experimental results verify the effectiveness of the proposed method.