0% Complete
Home
/
13th International Conference on Computer and Knowledge Engineering
Multi Model CNN Based Gas Meter Characters Recognition
Authors :
Sanaz Tarhib
1
Jafar Tanha
2
Soodabeh Imanzadeh
3
Sahar Hassanzadeh Mostafaei
4
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.
Papers List
List of archived papers
Mitochondrial Segmentation in Microscopy Images Using UNet-VGG19
Zerek Sediq Hossein - Rojiar Pir Mohammadiani - Saadat Izadi
Automated Person Identification from Hand Images\\using Hierarchical Vision Transformer Network
Zahra Ebrahimian - Seyed Ali Mirsharji - Ramin Toosi - Mohammad Ali Akhaee
A New Time Series Approach in Churn Prediction with Discriminatory Intervals
Hedieh Ahmadi - Seyed Mohammad Hossein Hasheminejad
Frame Classification in Video Capsule Endoscopy Using an Improved Capsule Network
Amirhossein Ghaemi - Habibollah Danyali - Alireza Ghaemi
An interactive user groups recommender system based on reinforcement learning
Hediyeh Naderi Allaf - Mohsen Kahani
Generating Hand-Written Symbols With Trajectory Planning Using A Robotic Arm
Arya Parvizi - Armin Salimi-Badr
Towards Efficient Capsule Networks through Approximate Squash Function and Layer-wise Quantization
Mohsen Raji - Kimia Soroush - Amir Ghazizadeh
An overview of Business Intelligence research in healthcare organizations using a topic modeling approach
Mohammad Mehraeen - Laya Mahmoudi - Mohammad Hossein Sharifi
Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm
Zaniar Sharifi - Khabat Soltanian - Ali Amiri
A Cost-Sensitive Genetic Algorithm for Customer Segmentation in Auto Insurances
Alireza Khajenoori - Mohammad Saniee Abadeh - Mohsen Mohammadzadeh
more
Samin Hamayesh - Version 41.5.3