A mobile recognition system for analog energy meter scanning

Abstract

The work presents a mobile platform based system, scanning electricity, gas and water meters. The motivation is the automation of the manual procedure, increasing the reading accuracy and decreasing the human effort. The methodology comprises two stages - digits detection and Optical Character Recognition. The detection of digits is accomplished by a pipeline of operations. Optical Character Recognition is achieved, employing two different approaches - Tesseract OCR and Convolutional Neural Network. The performance evaluation on a vast number of images reports high precision for the algorithms of both stages. Furthermore, Convolutional Neural Network significantly outperforms the Tesseract OCR for all types of meters. The objective of functionality by the limited speed and data storage of mobile devices is also successfully met.