PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration

Abstract

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 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 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.

Keywords

• Digital pressure gauge calibration
• automatic meter reading
• sequence text recognition
• light weight CNN
• digital gauge dataset