Abstract: Existing pointer meter reading algorithms usually detect the scale of the meter to identify the value. However, in the meter image, the scale contains less features, which is prone to misdetection. To solve this problem, a new automatic reading algorithm of pointer meter is proposed. This algorithm greatly improves the robustness of meter reading recognition by selecting image features of a larger area. Because the pointer scale value text is a common part of various meters and has far more image features than scale, the proposed algorithm uses the scale value text as the recognition basis. Firstly, the convolutional neural network detects the scale value text in the meter image, and uses its position coordinates to fit the center of the meter. The secondary image correction converts the arc-shaped scale area into a horizontal straight-line area. At the same time, the recognized text value is also used to improve the distance interpretation method. This method is compared with other reading algorithms. The comparison experiment proves, this algorithm has a high reading accuracy rate, its reference error is less than 0.5%, and it has higher robustness under complex shooting conditions.

Key words: pointer meter, meter reading, text detection, secondary correction, distance interpretation method