Deep Learning and Optical Character Recognition for Digitization of Meter Reading

Yue Jiet Chong, Kein Huat Chua, Mohammad Babrdel, Lee Cheun Hau, Li Wang

研究成果: Conference contribution

摘要

One of the important steps in digital transformation is to make all the instrumental devices connected to the cloud. However, replacing the existing analogue meters with the cloud-connected digital meters can be very costly especially for industrial grade meters. In this project, a deep learning model based on Single Shot Detector (SSD) MobileNet V2 and an optical character recognition (Tesseract OCR) engine are developed for the low-cost digitization of analogue meter readings. The deep learning model is trained with a dataset of 750 meters' images, and it is used to detect the region of interest where the meter's readings are located. The OCR is used to convert the readings to string datatype. The image processing techniques via the OpenCV library are adopted for enhancing the quality of the region of interest (ROI). The model is developed in Python and the evaluation is carried for various types of meters, illumination conditions, and backgrounds. The results show that the deep learning model and OCR accuracies are 95% and 93%, respectively.

原文English
主出版物標題2022 12th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2022
發行者Institute of Electrical and Electronics Engineers Inc.
頁面7-12
頁數6
ISBN(電子)9781665487030
DOIs
出版狀態Published - 2022
事件12th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2022 - Virtual, Online, Malaysia
持續時間: 2022 5月 212022 5月 22

出版系列

名字2022 12th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2022

Conference

Conference12th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2022
國家/地區Malaysia
城市Virtual, Online
期間22-05-2122-05-22

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦網路與通信
  • 電腦科學應用
  • 電氣與電子工程
  • 工業與製造工程
  • 儀器
  • 電腦視覺和模式識別

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