| Summary: | While smart meters are still not widely installed in many countries, automatic reading of traditional-type meters is useful from the perspective of both cost and safety. Although convolutional neural network (CNN) showed a high potential for automatic meter reading under unconstrained environment, it is facing various challenges. One is the difficulty of collecting a sufficient amount of training dataset since some digits of a meter may take a long time to update. Another challenging issue is how to recognize the transitional state between two consecutive numbers. To solve these problems, we propose a new data augmentation technique that can automatically generate annotated images of numbers, including the transitional states. By taking advantage of the state-of-the-art generative neural network model, the generated numbers resemble the local appearance of those in the original meter images. Evaluation experiments confirm that our proposed generative data augmentation techniques improve the robustness of the recognition model and achieve outstanding results when compared to the previous work.
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