A Mobile Recognition System for Analog Energy Meter Scanning

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• 关键词：Analog energy meter scanning ; Water ; Gas ; Electricity meters ; OCR ; Tesseract ; Convolutional Neural Network
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10072
• 期：1
• 页码：247-256
• 丛书名：Advances in Visual Computing
• ISBN：978-3-319-50835-1
• 卷排序：10072

文摘

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.