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Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks
2021
Applied Sciences
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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). ...
The simulation results show very high performance; the recognition rate was close to 100%. ...
Acknowledgments: This project was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University under the research project # 2017/01/7551. ...
doi:10.3390/app11041573
fatcat:vsryidj2z5fypij4babgo5s3ia
Generative Adversarial Network for an Improved Arabic Handwritten Characters Recognition
2022
International journal of advances in soft computing and its applications
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Furthermore, this discriminator is used as a classifier in most generative adversarial networks by employing the binary sigmoid cross-entropy loss function. ...
This research proposes employing sigmoid cross-entropy to recognize Arabic handwritten characters using multi-class GANs training algorithms. ...
and the discriminator network) and the traditional deep conventional neural network which is used to train the OCR model. ...
doi:10.15849/ijasca.220328.12
fatcat:mvhrlnr4nvehhgwth3ji7ymofq
Recognition of Handwritten Characters using Deep Convolutional Neural Network
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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These factors also increase the complexity of handwritten character recognition. This paper proposes a new approach to identify handwritten characters for Telugu language using Deep Learning (DL). ...
The proposed work can be enhance the recognition rate of individual characters. The proposed approach recognizes with overall accuracy is 94%. ...
The objective of this work is to effectively extract the topo-logical features for handwritten numerals recognition using convolutional Neural Network (CNN) with deep network. ...
doi:10.35940/ijitee.f1064.0486s419
fatcat:kr3tvm464vdcth4xm22rvrqp3m
HANDWRITTEN AMHARIC CHARACTER RECOGNITION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS
2019
E-journal of New World Sciences Academy
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In order to recognize handwritten Amharic character a novel method based on deep neural networks is used which has recently shown exceptional performance in various pattern recognition and machine learning ...
The use of CNN leads to significant improvements across different machinelearning classification algorithms. ...
A handwritten Hangul character recognition system using deep convolutional neural network by proposing several novel techniques to increase the performance and training speed of the networks is done by ...
doi:10.12739/nwsa.2019.14.2.1a0433
fatcat:27puhli73zh2fpfd2c2ttvwi3y
Neural-network classifiers for recognizing totally unconstrained handwritten numerals
1997
IEEE Transactions on Neural Networks
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Index Terms-Handwritten numeral recognition, multiple neural networks, hidden Markov models, hybrid classifiers, selforganizing feature maps. ...
pattern recognition are largely inadequate for difficult problems such as handwritten numeral recognition. ...
Lee, graduate students in the AI laboratories at Yonsei University, for their support of the implementation of the algorithms and the simulation performed for this research. ...
doi:10.1109/72.554190
pmid:18255609
fatcat:at2ffqavxffinjrnooj6gmnwge
A Novel Interpolation Perspective for Handwritten Digit Recognition using Neural Network
2020
International journal of recent technology and engineering
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To train those models, research work includes Convolutional Neural Network (CNN), Dynamic Neural Network(DNN), Recurrent Neural Network(RNN), and TensorFlow algorithms using Keras , which can be accurately ...
In this work, we present an innovative technique for manually written character recognition that is disconnected, using deep neural networks. ...
This project -A Novel Interpolation Approach for Handwritten Digit Recognition Using Neural Network‖ has used some algorithms for the project to make it work perfectly which consists of the linear discriminant ...
doi:10.35940/ijrte.b3148.079220
fatcat:jshf2cz2djdvvktfq2qel2e75i
Integrated segmentation and recognition of handwritten numerals with cascade neural network
1999
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
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In the proposed method, a new type of cascade neural network is developed to train the spatial dependences in connected handwritten numerals. ...
This cascade neural network was originally extended from the multilayer feedforward neural network to improve the discrimination and generalization power. ...
The recognition process decides the best numeral string by verification using recognizer. The recognition accuracy of this system depends on the accuracy of the single digit recognizer. ...
doi:10.1109/5326.760572
fatcat:v6e5xqhagna3ngidre2kghx7ey
Generative Adversarial Networks as a Data Augmentation Tool for Handwritten Digits
2023
International Journal on Recent and Innovation Trends in Computing and Communication
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In the field of data processing, handwritten digit recognition (HDR) has proven to be of great use. ...
For instance, a lot of labelled examples are needed to train a model in deep learning approaches, where all the feature extraction steps are learned within the artificial neural network. ...
To improve upon the initial GAN's discriminator, WGANs implement a critic network. ...
doi:10.17762/ijritcc.v11i5.6606
fatcat:nbedjcepvjbmre2hueu53mt4m4
Convolutional Neural Network based Handwritten Bengali and Bengali-English Mixed Numeral Recognition
2016
International Journal of Image Graphics and Signal Processing
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Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. ...
The existing Bengali numeral recognition methods used distinct feature extraction techniques and various classification tools. ...
Ujjwal Bhattacharya (Computer & Communication Sciences Division, Indian Statistical Institute, Kolkata, India) for providing the benchmark dataset used in this study. ...
doi:10.5815/ijigsp.2015.09.06
fatcat:h4idhcdrnnhydcjbiekixbd2ee
BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks
[article]
2019
arXiv
pre-print
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BDNet is a densely connected deep convolutional neural network model used to classify (recognize) Bengali handwritten numeral digits. ...
It is end-to-end trained using ISI Bengali handwritten numeral dataset. ...
Acknowledgment First of all, the authors of the BDNet are thankful to CVPR Unit, Indian Statistical Institute, Kolkata, for providing the dataset for the academic research. ...
arXiv:1906.03786v4
fatcat:bqqzqjzw7jfu7m5olwlbw7a2va
Handwritten Mathematical Expressions Recognition using Back Propagation Artificial Neural Network
2016
Communications on Applied Electronics
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Keywords Character recognition, Math symbol recognition, Handwritten math equations, Feed forward back propagation neural network. ...
to accuracy of the experimental results including lessen efforts. ...
Multilayer perceptron network trained by error back propagation algorithm is superior in recognition accuracy. In proposed system after initialize the neural network, train it with training set. ...
doi:10.5120/cae2016652125
fatcat:phjlg4gqjbb2fcokdeirndlhci
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters
[article]
2019
arXiv
pre-print
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We experimentally demonstrate the effectiveness of our approach by classifying noisy versions of MNIST, handwritten Bangla Numeral, and Basic Character datasets. ...
For classification, we progressively train a classifier generative adversarial network on the characters from low to high resolution. ...
Introduction Early work in neural networks focused on classification of handwritten characters [16, 17] . Since then, there has been a lot of research on character recognition. ...
arXiv:1908.08987v1
fatcat:fcpiai5pcne3lcaujjd2ymzxki
Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
2019
Applied Sciences
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In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. ...
The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app9132758
fatcat:ymmudtesgjf3bch2fqaoy3cf3i
Off-Line Handwritten Character Recognition System Using Support Vector Machine
2017
American Journal of Neural Networks and Applications
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In this proposed work an efficient Support Vector Machine based off-line handwritten character recognition system has been developed. ...
Selection of classifiers and feature extraction methods has a prime role in achieving best possible classification accuracy in character recognition system. ...
SVM is one of the classifier used by the authors [17] for the recognition of Indian and Arabic handwritten numeral characters. Also a hybrid MLP-SVM model has been proposed by Washington W. ...
doi:10.11648/j.ajnna.20170302.12
fatcat:ugddtpn3evfnjdgfuozhjbxm5m
Drawing and Recognizing Chinese Characters with Recurrent Neural Network
[article]
2016
arXiv
pre-print
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In this paper, we propose a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generative model for drawing (generating) Chinese ...
The generated characters (in vector format) are human-readable and also can be recognized by the discriminative RNN model with high accuracy. ...
ACKNOWLEDGMENTS The authors thank the developers of Theano [44] , [45] for providing such a good deep learning platform. ...
arXiv:1606.06539v1
fatcat:ny7tle5szrfq3pacz5ejwe3dza
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