Abstract
This paper describes a method to localize and recognize seven-segment displays on digital energy meters. Color edge detection is first performed on a camera-captured image of the device which is then followed by a run-length technique to detect horizontal and vertical lines. The region of interest circumscribing the LCD panel is determined based on the attributes of intersecting horizontal and vertical lines. The extracted display region is preprocessed using the morphological black-hat operation to enhance the text strokes. Adaptive thresholding is then performed and the digits are segmented based on stroke features. Finally, the segmented digits are recognized using a support vector machine classifier trained on a set of syntactic rules defined for the seven-segment font. The proposed method can handle images exhibiting uneven illumination, the presence of shadows, poor contrast, and blur, and yields a recognition accuracy of 97% on a dataset of 175 images of digital energy meters captured using a mobile camera.
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Kasar, T. (2019). Recognition of Seven-Segment Displays from Images of Digital Energy Meters. In: Nagabhushan, P., Guru, D., Shekar, B., Kumar, Y. (eds) Data Analytics and Learning. Lecture Notes in Networks and Systems, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-2514-4_1
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DOI: https://doi.org/10.1007/978-981-13-2514-4_1
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