Read Pointer Meters Based on a Human-Like Alignment and Recognition Algorithm

Recently, developing an automatic reading system for analog measuring instruments has gained increased attention, as it enables the collection of numerous types of equipment. Nonetheless, two major obstacles still obstruct its deployment to real-world applications. The first issue is that they rarely take the entire pipeline’s speed into account. The second is that they are incapable of dealing with some low-quality images (i.e., meter breakage, blur, and uneven scale). In this paper, we propose a human-like alignment and recognition algorithm to overcome these problems. More specifically, a spatial transformed module (STM) is proposed to obtain the front view of images in a self-autonomous way based on an improved spatial transformer network (STN). Meanwhile, a value acquisition module (VAM) is proposed to infer accurate meter values by an end-to-end trained framework. In contrast to previous research, our model aligns and recognizes meters totally implemented by learnable processing, which mimics human behaviors and thus achieves higher performance. Extensive results verify the robustness of the proposed model in terms of accuracy and efficiency. The code and the dataset is available in https://github.com/shuyansy/Detect-and-read-meters.