Automatic digital pressure gauge calibration is challenging due to various unconstrained conditions.Although existing CNN-RNN based methods have been almost perfect on scene text recognition, they fail to perform well on digital pressure gauge calibration jeff rosenstock buffalo that requires to be extremely computation-efficient and accurate.In this paper, we propose a light weight fully convolutional sequence recognition network for fast and accurate digital Pressure Gauge Calibration (PGC-Net).PGC-Net integrates feature extraction, sequence modelling and transcription into a unified framework.
Experimental results show that PGC-Net runs 28 fps on CPU with 97.41% accuracy.Compared with previous methods, PGC-Net achieves better or comparable performance at lower inference time.Without bells and whistles, PGC-Net is quadruple ointment for dogs capable of recognizing decimal points that usually appear in pressure gauge images, which evidently verifies the feasibility of PGC-Net.
We collected a dataset that contains 17, 240 gauge images with annotated labels for automatic digital pressure gauge calibration.The dataset has been public for future research.