Developed countries are facing many challenges to convert large areas of existing services to electronic modes, reflecting the current nature of workflow and the equipment utilized for achieving such services. For instance, electricity bill collection still tend to be based on traditional approaches (paper-based and relying on human interaction) making them comparatively time-consuming and prone to human error.
This research aims to recognize numbers in mechanical electricity meters and convert them to digital figures utilizing Optical Character Recognition (OCR) in Matlab. The research utilized the location of red region in color electricity meters image to determine the crop region that contain the meters numbers, then extracts this numbers region and convert it into binary image and extract the numbers as a text using OCR technique.
A camera for the Iphone 6 (8-megapixel) is used to take a snapshot of the meter screen. The red box in the meter is used to calculate the window coordinates (vertical and horizontal length) that contain the numbers in the original image. The results show a high level of accuracy, reaching 100% due to the effort done on pre-processing the digital images before feeding the part that contains the numbers into the OCR engine. Compared to the maximum accuracy obtained in other previous research of less than 100% in most of related works, the suggested method provide better approach to obtain the optimum results .
Despite the strong results, some challenges still need to be investigated further to find the best solutions, for example the issue of scratched or unclear meter screen, also the meter type 2 (the type that do not have the red box)
