# Easy Mobile Meter Reading for Non-Smart Meters: Comparison of AWS Rekognition and Google Cloud Vision Approaches

arXiv: https://arxiv.org/abs/1910.12617 · HTML: https://mlsanity.com/paper/1910.12617/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Towards computer vision technologies: Semi-automated reading of automated utility meters](https://mlsanity.com/paper/2211.13483/) (2022)
- [Analysis of LLM Performance on AWS Bedrock: Receipt-item Categorisation Case Study](https://mlsanity.com/paper/2604.01615/) (2026)

## Abstract (arXiv)

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 (1) replacement of the existing meters, even when they are functional and new, and (2) large changes of the whole system dealing with the meter readings. This paper presents results of a project on automation of the meter reading process for the standard (non-smart) meters using computer vision techniques, focusing on the comparison of two computer vision techniques, Google Cloud Vision and AWS Rekognition.