Recently, developing an automatic reading system for analog measuring instruments has gained increased attention, as it enables the collection of numerous state 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 Networks(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's behaviours and thus achieves higher performances. Extensive results verify the good robustness of the proposed model in terms of the accuracy and efficiency.

翻译：近年来，开发模拟测量仪表的自动读数系统因其能采集大量设备状态而备受关注。然而，两个主要障碍仍阻碍其在实际应用中的部署：其一，现有方法鲜少考虑整个流程的运行速度；其二，它们难以应对部分低质量图像（如仪表破损、模糊及刻度不均）。本文提出一种类人对齐与识别算法以解决上述问题。具体而言，通过改进的空间变换网络（STN），提出空间变换模块（STM）以实现图像前视图的自适应获取；同时，提出数值获取模块（VAM），通过端到端训练框架推导精确仪表读数。与以往研究不同，本模型完全通过可学习过程实现仪表对齐与识别，模拟人类行为从而获得更高性能。大量实验验证了该模型在准确性与效率方面的良好鲁棒性。