International Conference on Computer and Knowledge Engineering

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Multi Model CNN Based Gas Meter Characters Recognition

Authors :

Sanaz Tarhib¹ Jafar Tanha² Soodabeh Imanzadeh³ Sahar Hassanzadeh Mostafaei⁴

1- Faculty of Electrical and Computer Engineering University of Tabriz 2- Faculty of Electrical and Computer Engineering University of Tabriz 3- Faculty of Electrical and Computer Engineering University of Tabriz 4- Faculty of Electrical and Computer Engineering University of Tabriz

Keywords :

Recognition،Detection،Convolutional Neural Network،Long Short-Term Memory،Gated Recurrent Unit

Abstract :

The recognition and extraction of text from natural scene images is a highly challenging task in the field of computer vision. Convolutional neural networks (CNNs) have been shown to be highly effective in recognizing characters and words from images as they can perceive the structural patterns of characters and words. This makes CNNs one of the most suitable approaches for solving recognition problems, such as text recognition in natural scene images. In this study, we aim to recognize numerical texts from images and employ three models for this task: CNN models, a combination of CNN-LSTM models, and a combination of CNN-GRU models. The dataset used in this study comprises images taken from gas meters, which were collected by our team using different phones at different times. Our results show that the accuracy achieved by the CNN, CNN-LSTM, and CNN-GRU models in recognizing numerical texts from images is 72.9%, 96.6%, and 97.63%, respectively. These findings suggest that the CNN-LSTM and CNN-GRU models are highly effective in recognizing numerical texts in images, with the CNN-GRU model exhibiting the highest accuracy. Overall, these results demonstrate the potential of using deep learning models for recognizing numerical texts in images, particularly the combination of CNN and Gated recurrent unit (GRU) models.

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