Introduction: Instead of replacing existing analogue water meters with Internet of Things (IoT) connected substitutes, an alternative would be to attach an IoT connected module to the analogue water meter that optically reads the meter value using Optical Character Recognition (OCR). Such a module would need to be battery-powered given that access to the electrical grid is typically limited near water meters. Research has shown that offloading the OCR process can reduce the power dissipation from the battery, and that this dissipation can be reduced even further by reducing the amount of data that is transmitted. 

Purpose: For the sake of minimising energy consumption in the proposed solution, the purpose of the study is to find out to what extent it is possible to reduce an input image’s file size by means of resolution, colour depth, and compression before the Google Cloud Vision OCR engine no longer returns feasible results.  

Method and implementation: 250 images of analogue water meter values were processed by the Google Vision Cloud OCR through 38 000 different combinations of resolution, colour depth, and upscaling. 

Results: The highest rate of successful OCR readings with a minimal file size were found among images within a range of resolutions between 133 x 22 to 163 x 27 pixels and colour depths between 1- and 2-bits/pixel. 

Conclusion: The study shows that there is a potential for minimising data sizes, and thereby energy consumption, by offloading the OCR process by means of transmitting images of minimal file size.