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Using Stanford CoreNLP Capabilities for Semantic Information Extraction from Textual Descriptions Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Comparison of Computer Vision Approaches in Application to the Electricity and Gas Meter Reading

Authors : Maria Spichkova, Johan van Zyl, Siddharth Sachdev, Ashish Bhardwaj, Nirav Desai

Published in: Evaluation of Novel Approaches to Software Engineering

Publisher: Springer International Publishing

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Abstract

This chapter presents comparison of computer vision approaches in application to the meter reading process for the standard (non-smart) electricity and gas. In this work, we analyse four techniques, Google Cloud Vision, AWS Rekognition, Tesseract OCR, and Azure’s Computer Vision. Electricity and gas meter reading is a time consuming task, which is done manually in most cases. There are some approaches proposing use of smart meters that report their readings automatically. However, this solution is expensive and requires both replacement of the existing meters, even when they are functional and new, and extensive changes of the whole meter reading system dealing.

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Metadata
Title
Comparison of Computer Vision Approaches in Application to the Electricity and Gas Meter Reading
Authors
Maria Spichkova
Johan van Zyl
Siddharth Sachdev
Ashish Bhardwaj
Nirav Desai
Copyright Year
2020
Book
Evaluation of Novel Approaches to Software Engineering

Print ISBN: 978-3-030-40222-8

Electronic ISBN: 978-3-030-40223-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-40223-5_15

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