  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.
