International Conference on Computer and Knowledge Engineering

Home / 13th International Conference on Computer and Knowledge Engineering

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.

Papers List

List of archived papers

XAI for Transparent Autonomous Vehicles: A New Approach to Understanding Decision-Making in Self-driving Cars

Maryam Sadat Hosseini Azad - Amir Abbas Hamidi Imani - Shahriar Baradaran Shokouhi

Vaccine Distribution Modelling in Pandemics through Multi-Agent Systems: COVID-19 Case

Hossein Yarahmadi - Mohammad Ebrahim Shiri - Hamid Reza Navidi - Arash Sharifi - Moharram Challenger - Hassan Piriaei

Evaluating the Impact of Traveling on COVID-19 Prevalence and Predicting the New Confirmed Cases According to the Travel Rate Using Machine Learning: A Case Study in Iran

Anita Ghandehari - Soheil Shirvani - Hadi Moradi

EEMC: Energy Efficient Multi-Clustering Using Grey Wolf Optimizer in WSNs

Maryam Ghorbanvirdi - Sayyed Majid Mazinani

Improve the utility of tensor cores by compacting sparse matrix technique

Mohammad.S Abazari - Mahsa Zahedi - Abdorreza Savadi

Optimizing Question-Answering Framework Through Integration of Text Summarization Model and Third-Generation Generative Pre-Trained Transformer

Ervin Gubin Moung - Toh Sin Tong - Maisarah Mohd Sufian - Valentino Liaw - Ali Farzamnia - Farashazillah Yahya

Automated software design using Machine Learning With Natural Language Processing

Fahimeh Khedmatkon - Seyed Mohammad Hossein Hasheminejad - Jaleh Shoshtarian Malak

The application of Brain Drain Optimization algorithm on static drone placement problem

Mohammad Mehdi Samimi - Alireza Basiri

Word-level Persian Lipreading Dataset

Javad Peymanfard - Ali Lashini - Samin Heydarian - Hossein Zeinali - Nasser Mozayani

City Intersection Clustering and Analysis Based on Traffic Time Series

Mohammad Aminazadeh - Fakhroddin Noorbehbahani