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Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks [article]

Akm Ashiquzzaman, Abdul Kawsar Tushar, Md Ashiqur Rahman
2022 arXiv   pre-print
A convolutional neural network model for recognizing handwritten numerals in Arabic language is proposed in this paper, where the dataset is subject to various augmentation in order to add robustness needed  ...  for deep learning approach.  ...  The CMA-TERDB 3.3.1 Arabic handwritten digit dataset is used [8] . It contains 3000 separate handwritten numeral image. Each of them is 32x32 pixel RGB image.  ... 
arXiv:1708.05969v5 fatcat:waarcqza4bbwffeq6ibkrdpgg4

Handwritten Arabic Numeral Recognition using Deep Learning Neural Networks [article]

Akm Ashiquzzaman, Abdul Kawsar Tushar
2017 arXiv   pre-print
In this paper, we propose a novel algorithm based on deep learning neural networks using appropriate activation function and regularization layer, which shows significantly improved accuracy compared to  ...  the existing Arabic numeral recognition methods.  ...  DATASET Deep learning is solely depended on the data and hence it needs a large amount of data to function properly.  ... 
arXiv:1702.04663v1 fatcat:if7dvel6c5hqfdzox63ypwubda

A Pen Based Intelligent System for Educating Arabic Handwriting

Mohamed Loey
2020 Figshare  
Theproposed system was designed and developed for automatically recognizinghandwritten Arabic characters and digits using deep learning architectures.The system also detects and diagnoses Arabic preschool  ...  digits recognition , this work presents a deeplearning architecture called Convolutional Neural Network (CNN).  ...  For Arabic handwritten digits recognition , this work presents a deep learning architecture called Convolutional Neural Network (CNN).  ... 
doi:10.6084/m9.figshare.12249056.v1 fatcat:xro3pxjfi5hutpaxrsqz2slt2i

Deep Learning Autoencoder Approach for Handwritten Arabic Digits Recognition [article]

Mohamed Loey, Ahmed El-Sawy, Hazem EL-Bakry
2017 arXiv   pre-print
This paper presents a new unsupervised learning approach with stacked autoencoder (SAE) for Arabic handwritten digits categorization.  ...  Recently, Arabic handwritten digits recognition has been an important area due to its applications in several fields.  ...  Deep learning based on algorithms using multilayer network such as deep neural networks, convolutional deep neural networks, deep belief networks, recurrent neural networks and stacked autoencoders.  ... 
arXiv:1706.06720v1 fatcat:htd4ruhnofb7fmzkhnvotkmtru

Handwriting Text Recognition using Neural Networks

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The major method used to recognize text is the Convolutional neural network (CNN) as a deep learning classifier.  ...  The other techniques used are Recurrent Neural Network (RNN) and a custom developed model called deep-writer, which is a variant of CNN architecture.  ...  In this paper they have proposed an algorithm which is built using deep learning neural networks for handwritten Arabic numerals recognition.  ... 
doi:10.35940/ijitee.b7705.129219 fatcat:ojmum6lhunh5vaxrrbs7qbcs7q

Arabic Handwritten Digit Recognition using Convolutional Neural Network

2020 International journal of recent technology and engineering  
The proposed method on the deep learning technique is Convolutional Neural Network.  ...  LeNet-5 architect also used in training and recognizing the handwritten image of Arabic numeral as much as 70000 images derived from MADbase dataset.  ...  Recognition As mention in the previous subsection, to recognize Arabic handwritten digit, this research has used the convolutional neural network as an identifier.  ... 
doi:10.35940/ijrte.f7745.038620 fatcat:rjyfcx2a3zev3mfg5gi5r5vypa

CNN for Handwritten Arabic Digits Recognition Based on LeNet-5

Mohamed Loey
2020 Figshare  
The paper provided a deep learning technique that can be effectively apply to recognizing Arabic handwritten digits.  ...  LeNet-5, a Convolutional Neural Network (CNN) trained and tested MADBase database (Arabic handwritten digits images) that contain 60000 training and 10000 testing images.  ...  So, the propose of the paper is using CNN to create deep learning recognition system for Arabic handwritten digits recognition.  ... 
doi:10.6084/m9.figshare.12236897.v1 fatcat:j7xzm5keofb4jeeduuxujimqwm

A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks [article]

Abdul Kawsar Tushar, Akm Ashiquzzaman, Afia Afrin, Md. Rashedul Islam
2017 arXiv   pre-print
This model is presented for the recognition of handwritten numerals in Indic languages.  ...  The output performance of the proposed neural network is shown to have closely matched other state-of-the-art methods using only a fraction of time used by the state-of-the-arts.  ...  Deep Neural Network This section presents an overview of the deep learning system that will be used in the proposed model.  ... 
arXiv:1707.08385v1 fatcat:uvj5sex5hzcyhdwxra622cek4a

Online Arabic Handwriting Characters Recognition using Deep Learning

Khalid Mohammed Musa Yaagoup, Mohamed Elhafiz Mustafa
2020 IJARCCE  
We presented a Convolutional Neural Network (CNN) model for the recognition of Arabic handwritten characters in this paper.  ...  It is based on algorithms using multilayer network such as deep neural networks, convolutional deep neural networks, deep belief networks, recurrent neural networks and stacked autoencoders.  ...  Deep neural network model(Convolutional Neural Network (CNN)) The neural network used for Arabic character recognition is outlined in this section.  ... 
doi:10.17148/ijarcce.2020.91014 fatcat:zptqvagahbh47hrqmehcxlydwy

Arabic handwritten digits recognition based on convolutional neural networks with resnet-34 model

Rasool Hasan Finjan, Ali Salim Rasheed, Ahmed Abdulsahib Hashim, Mustafa Murtdha
2021 Indonesian Journal of Electrical Engineering and Computer Science  
In this paper, a new method is proposed that used pre-trained convolutional neural networks with resnet-34 model what is known as transfer learning for recognizing digits in the arabic language that provides  ...  us a high accuracy when this type of network is applied.  ...  recognition using deep learning neural networks" [12] . 97.4% "An Efficient Recognition Method for Handwritten Arabic Numerals Using CNN with Data Augmentation and Dropout" [16] . 99.4% Proposed method  ... 
doi:10.11591/ijeecs.v21.i1.pp174-178 fatcat:n56ab4z3jjdjfnqamfvv2hdpuy

Digital Recognition Methods Based on Deep Learning

Yuanbo Peng, Lianhui Li
2022 Scientific Programming  
Taking the learning process of the handwritten numeral recognition algorithm based on deep learning as a clue, from deep learning to convolutional neural network, from simple to deep, the relevant basic  ...  The neural network of deep learning is established with TensorFlow to realize the classification and recognition of handwritten numbers.  ...  Deep Learning Algorithms-Classification of Neural Networks.  ... 
doi:10.1155/2022/9691331 fatcat:jorgvzswqrfl7bivyoyjvvsmwi

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR) [article]

Jamshed Memon, Maira Sami, Rizwan Ahmed Khan
2020 arXiv   pre-print
During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format.  ...  Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth.  ...  Deep, big, simple neural nets for handwritten digit recognition. 784 2010 [30] Diagonal based feature extraction for handwritten character recognition system using neural network. 175 2011 [31] Convolutional  ... 
arXiv:2001.00139v1 fatcat:p3rdutz35besxfxf7suozt7r2u

Generative Adversarial Network for an Improved Arabic Handwritten Characters Recognition

Yazan Alwaqfi, Mumtazimah Mohamad, Ahmad Al-Taani
2022 International journal of advances in soft computing and its applications  
Keywords: Generative Adversarial Networks (GANs), Arabic Characters, Optical Character Recognition, Convolutional Neural Networks (CNNs).  ...  This research proposes employing sigmoid cross-entropy to recognize Arabic handwritten characters using multi-class GANs training algorithms.  ...  For instance, Deep learning classifiers are used to attain the best recognition accuracy in the Arabic language since deep learning produces reasonably accurate results in the English language.  ... 
doi:10.15849/ijasca.220328.12 fatcat:mvhrlnr4nvehhgwth3ji7ymofq

Handwritten Arabic Digit Recognition Using Convolutional Neural Network

Jawad Hasan Yasin AlKhateeb
2020 International Journal of Communication Networks and Information Security  
The concept of deep learning via the convolutional neural network (CNN) with the ADBase database is used to achieve the goal. The training is done by having a 3*3 and 5*5 filters.  ...  Basically, while the classification phase distinct learning rates are used to train the network. The obtained results are encouraging and promising.  ...  Tushar [14] proposed a novel algorithm using deep learning neural networks to improve and modify the recognition accuracy in recognizing Arabic numeral. Saad Bin Ahamd et al.  ... 
dblp:journals/ijcnis/AlKhateeb20 fatcat:4qlrliksirbnhjzr5jkgkw4hxi

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Amin Alqudah, Ali Mohammad Alqudah, Hiam Alquran, Hussein R. Al-Zoubi, Mohammed Al-Qodah, Mahmood A. Al-Khassaweneh
2021 Applied Sciences  
In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs).  ...  Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields.  ...  In [18] , the authors used Spiking neural networks (SNNs) in the recognition of handwritten numerals, where they were able to improve the recognition accuracy compared to the use of BP neural networks  ... 
doi:10.3390/app11041573 fatcat:vsryidj2z5fypij4babgo5s3ia
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