Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: Accurate water usage information is very important for municipalities in order to provide accurate billing information for high volume water users. Meter reading are currently obtained by sending a person out to every meter to obtain a manual reading. This is very costly with regards to time and money, and it is also very error prone. In order to improve on this system, an image based telemetry system was developed that can be retrofitted on currently installed bulk water meters. Images of the meter dials are captured and transmitted to a central server where they are further processed and enhanced. Character recognition is performed on the enhanced images in order to extract meter readings. Through tests it was found that characters can be recognised to 100% accuracy for cases which the character recognition software has been trained, and 70% accuracy for cases which is was not trained. Thus, an overall recognition accuracy of 85% was achieved. These results can be improved upon in future work by statistically analysing results and utilizing the inherent heuristic information from the meter dials. Overall the feasibility of the approach was demonstrated and a way forward was indicated. / AFRIKAANSE OPSOMMING: Dit is belangrik vir munisipaliteite om akkurate water verbruikingssyfers te hê sodat hulle akkurate rekeninge aan hoë volume water gebruikers kan stuur. Tans besoek ’n persoon fisies elke meter om meterlesings te verkry. Dit is egter baie oneffektief ten opsigte van tyd en geld. Die metode is ook baie geneig tot foute. Ten einde te verbeter op hierdie stelsel was ’n beeld gebaseerde telemetrie stelsel ontwerp wat geïnstalleer word op huidig geïnstalleerde hoë volume water meters. Beelde van die meters word na ’n sentrale bediener gestuur waar dit verwerk word en die beeld kwaliteit verbeter word. Karakter herkenning sagteware word gebruik om die meter lesings te verkry vanuit die verbeterde beelde. Deur middel van toetse is gevind dat karakters herken kan word tot op 100% graad van akkuraatheid in gevalle waar die karakter herkenning sagteware opgelei is, en 70% akkuraatheid vir gevalle waarvoor dit nie opgelei was nie. Dus was ’n algehele herkennings akkuraatheid van 85% behaal. Hierdie resultate kan verbeter word in die toekoms deur die resultate statisties te analiseer en die inherente heuristieke inligting van die meter syfers te benutting. Ten slotte, in die tesis was die haalbaarheid van die benadering gedemonstreer en ’n weg vorentoe vir toekomstige werk aangedui.