GAS meter reading from real world images using a multi-net system

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• 作者：Marco Vanetti ; ^{marco.vanetti@uninsubria.it} ; Ignazio Gallo ^{ignazio.gallo@uninsubria.it} ; Angelo Nodari ^{angelo.nodari@uninsubria.it}
• 关键词：Object detection ; Object segmentation ; Text localization ; Ocr ; Neural networks ; Multi-net system
• 刊名：Pattern Recognition Letters
• 出版年：2013
• 出版时间：1 April, 2013
• 年：2013
• 卷：34
• 期：5
• 页码：519-526
• 全文大小：878 K

文摘

We present a new approach for automatic gas meter reading from real world images. The gas meter reading is usually done on site by an operator and a picture is taken from a mobile device as proof of reading. Since the reading operation is prone to errors, the proof image is checked offline by another operator to confirm the reading. In this study, we present a method to support the validation process in order to reduce the human effort. Our approach is trained to detect and recognize the text of a particular area of interest. Firstly we detect the region of interest and segment the text contained using a method based on an ensemble of neural models. Then we perform an optical character recognition using a Support Vector Machine. We evaluated every step of our approach, as well as the overall assessment, showing that despite the complexity of the problem our method provide good results also when applied to degraded images and can therefore be used in real applications.