Abstract
Abstract: To address the issue of manual reading, Automatic Meter Reading (AMR) is introduced, which is a combination of detection and recognition that is efficiently applied to identify the meter and accurately read the digits on electric meters. The initial step in meter detection is the collection of the dataset, after which the annotation process generates XML files that are converted to CSV files and the label map is generated. Using a pre-trained classifier SSD ResNet, the model is trained and evaluated. The identified photos are used as input meter images in meter recognition, where a sequence of pre-processing techniques is performed. After which, using tesseract and leptonica, a model is generated from source, and digit recognition is conducted. The accuracy of 95.45% is then generated after training and evaluating on 2000 images of different kinds of electric meters in various surroundings.
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