Automatic Generation of Seven-Segment Display Image for Machine-Learning-Based Digital Meter Reading

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Published Sep 4, 2023
Kota Gushima Takahiro Kashima

Abstract

This paper presents a novel approach for automating the reading of seven-segment displays using machine learning, specifically addressing the concern of acquiring training data. By developing an algorithm that can automatically generate training images, the need for seven-segment display digit image acquisition was significantly reduced, making the process more efficient and cost-effective. In addition, by automatically generating images, a large amount of training data can be acquired. The training images include noise, such as sunlight reflections, shadows, and blurring due to camera shaking. The proposed method employs a machine-learning model trained on a diverse dataset of synthetic images generated by an algorithm. This dataset includes various fonts and styles, enabling the model to predict the meter values displayed on various fonts of the seven-segment liquid crystal display. By leveraging this auto-generated image set, the model effectively eliminates the labor-intensive process of manually capturing and annotating real-world meter images. The experimental results demonstrated the effectiveness of the proposed approach, with a reading accuracy of 96.8%.

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Keywords

Seven-Segment Display, Machine Learning, Meter Reading

References
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778. doi: 10.1109/CVPR.2016.90

Wannachai, A., Boonyung, W., & Champrasert, P. (2020). Real-Time Seven Segment Display Detection and Recognition Online System Using CNN. In Y. Chen, T. Nakano, L. Lin, M. U. Mahfuz, & W. Guo (Eds.), Bioinspired Information and Communication Technologies Cham: Springer International Publishing, pp. 52-67. doi: 978-3-030-57115-3

Salomon, G., Laroca, R., & Menotti, D. (2020). Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines. Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1-8. doi: 10.1109/IJCNN48605.2020.9207318

Zheng, W., Yin, H., Wang, A., Fu, P., & Liu, B. (2017). Development of an automatic reading method and software for pointer instruments. Proceedings of 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS), pp. 1-6. doi: 10.1109/EIIS.2017.8298626

Liu, Y., Liu, J., & Ke, Y. (2020). A detection and recognition system of pointer meters in substations based on computer vision. Measurement, vol. 152, pp. 107333. doi: 10.1016/j.measurement.2019.107333

Vanetti, M., Gallo, I., & Nodari, A. (2013). GAS meter reading from real world images using a multi-net system. Pattern Recognition Letters, vol. 34, no. 5, pp. 519-526. doi: 10.1016/j.patrec.2012.11.014

Imran, M., Anwar, H., Tufail, M., Khan, A., Khan, M., & Ramli, D. A. (2023). Image-Based Automatic Energy Meter Reading Using Deep Learning. Computers, Materials & Continua, vol. 74, no. 1, pp. 203-216. doi: 10.32604/cmc.2023.029834
Section
Regular Session Papers