In this work we introduce a pipeline to detect and recognize various utility meter numbers in the wild. The system leverages on deep neural networks for detection and recognition. In the detection phase, we employ a fully Convolutional Neural Network to perform a pixel-wise classification, while the recognition phase employs another deep neural network to predict the length and individual digits in a meter. We qualitatively showed that the proposed approach is robust against severe perspective distortions, different lighting conditions and blurred images. Furthermore, it is capable of detecting small scale digits. Our approach is suitable for billing companies aiming to increase efficiency, lowering the time consumed by manual checks performed in the billing process. Finally, we release the dataset used in this work to benchmark the task.

Reading Meter Numbers in the Wild

Alessandro Calefati;Ignazio Gallo
;
Shah Nawaz
2019

Abstract

In this work we introduce a pipeline to detect and recognize various utility meter numbers in the wild. The system leverages on deep neural networks for detection and recognition. In the detection phase, we employ a fully Convolutional Neural Network to perform a pixel-wise classification, while the recognition phase employs another deep neural network to predict the length and individual digits in a meter. We qualitatively showed that the proposed approach is robust against severe perspective distortions, different lighting conditions and blurred images. Furthermore, it is capable of detecting small scale digits. Our approach is suitable for billing companies aiming to increase efficiency, lowering the time consumed by manual checks performed in the billing process. Finally, we release the dataset used in this work to benchmark the task.
9781728138572
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11383/2083817
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