Comparison of Computer Vision Approaches in Application to the Electricity and Gas Meter Reading This chapter presents comparison of computer vision approaches in application to the meter reading process for the standard (nonsmart) electricity and gas. In this work, we analyse four techniques, Google Cloud Vision, AWS Rekognition, Tesseract OCR, and Azurexe2x80x99s Computer Vision. 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 both replacement of the existing meters, even when they are functional and new, and extensive changes of the whole meter reading system dealing.

Fog Concentration Grade Judgment for Meter Reading Based on SVM Robot reading energy meter devices automatically is a basic task for smart grid. Unfortunately, the predicted accuracy is influenced by environment dramatically. In order to solve the problem, we propose a method which can metric fog concentration in environment before meter reading. Moreover, based on the characteristics of image and fog influence, joint features and SVM are used to discriminate the concentration level of foggy image. Specially, Contrast feature and DFT feature are extracted as joint features to describe the foggy image and SVM is used to study and train the joint feature. Finally the fog concentration level is discriminated naturally. The experimental results demonstrate the effectiveness of proposed method in term of judging the meter image with five level.