Research on Automatic Reading Recognition of Wheel Mechanical Water Meter Based on Improved U-Net and VGG16

Abstract

This paper proposes a deep learning scheme to automatically carry out reading recognition in wheel mechanical water meter images. Aiming at these early water meters deployed in old residential compounds, this method based on deep neural networks employs a coarse-to-fine reading recognition strategy, firstly, by means of an improved U-Net to locate the reading area of the dial on a large scale, and then the single character segmentation is performed according to the structural features of the dial, and finally carry out reading recognition through the improved VGG16. Experimental result shows that the proposed scheme can reduce the information interference of non-interested regions, effectively extract and identify reading results, and the recognition accuracy of 95.6% is achieved on the dataset in this paper. This paper proposes a new solution for the current situation of manual meter reading, which is time-consuming and labor-intensive, errors occur frequently; and the transformation cost is high and difficult to implement. It provides technical support for automatic reading recognition of wheel mechanical water meters.