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%.
Seven-Segment Display, Machine Learning, Meter Reading
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