Numeric CNNs: CNNs for Reading Numeric Characters on Meters

Abstract

Nowadays, Internet of Things (IoT) is becoming an irreplaceable role in human life. Moreover, the meter reading is becoming the procedure composed of picture collection and image recognition. Previous works treat meter reading as a problem of image classification. They only focus on the accuracy of classification but ignore numerical accuracy, which is the measurement’s essential performance. In this paper, we address that the meter reading is a hybrid regression and classification (HRC) problem. Under this definition, the resulting algorithm considers the targets of both measurement and digits recognition. To solve the HRC problem, we designed a hybrid regression and classification loss function and a multi-branch convolutional neural networks for numbers (N-CNNs). To further verify the effectiveness of the model’s classification and regression, we constructed two kinds of datasets: standard dataset and carry dataset. The N-CNNs establishes new state-of-the-art metrics both on regression and classification. Notably, the numerical precision of N-CNNs outperforms the classification-based methods. The numerical accuracy of the model has one order of magnitude higher than other models. Furthermore, we deployed N-CNNs in a realistic meter reading system based on smart meter shells and cloud computing.