40 research outputs found
Read Pointer Meters in complex environments 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 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
Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
Recently, models based on deep neural networks have dominated the fields of
scene text detection and recognition. In this paper, we investigate the problem
of scene text spotting, which aims at simultaneous text detection and
recognition in natural images. An end-to-end trainable neural network model for
scene text spotting is proposed. The proposed model, named as Mask TextSpotter,
is inspired by the newly published work Mask R-CNN. Different from previous
methods that also accomplish text spotting with end-to-end trainable deep
neural networks, Mask TextSpotter takes advantage of simple and smooth
end-to-end learning procedure, in which precise text detection and recognition
are acquired via semantic segmentation. Moreover, it is superior to previous
methods in handling text instances of irregular shapes, for example, curved
text. Experiments on ICDAR2013, ICDAR2015 and Total-Text demonstrate that the
proposed method achieves state-of-the-art results in both scene text detection
and end-to-end text recognition tasks.Comment: To appear in ECCV 201