Arabic handwritten datasets for pattern recognition and machine learning

Musa, Mohamed E. M.

doi:10.1109/icaict.2011.6110959

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…The newly used SUST ALT (Sudan University of Science and Technology – Arabic Language Technology group) database includes several Arabic handwritten databases. The digits database and the isolated letters database (34 classes) are created from scratch, while the source of the names database (40 classes) is the SUST graduation certificate application forms [ 19 , 33 ]. Table 2 , Table 3 and Table 4 present some raw samples of the experiments on databases based on their types while covering digits, characters, and words from the Arabic handwritten databases, respectively.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Multilayer DNN derivatives, such as stacked auto-encoder (SAE), deep belief network (DBN), recurrent neural network (RNN), and convolutional neural networks (CNNs), have proven their high performance and accuracy [ 10 , 28 , 31 , 32 ]. The CNN has shown that it outperforms state-of-the-art approaches [ 33 ] in various fields, including face recognition, object recognition, and image classification [ 22 , 23 , 34 , 35 , 36 ]. The CNN architecture is a multi-layer feed-forward neural network that adopts the back-propagation algorithm to learn and automatically extract features from high-dimensional and complex data such as images [ 9 , 37 , 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

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Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Ahmed

Gogate

Tahir

et al. 2021

Entropy

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Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including the high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we have introduced a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we have proposed a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters to prevent overfitting and further enhance its generalization performance when compared to conventional deep learning models. We employed numerous deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The proposed model was extensively evaluated, and it was observed to achieve excellent classification accuracy when compared to the existing state-of-the-art OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). Further comparative experiments were conducted on the respective databases using the pre-trained VGGNet-19 and Mobile-Net models; additionally, generalization capabilities experiments on another language database (i.e., MNIST English Digits) were conducted, which showed the superiority of the proposed DCNN model.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Ahmed

Gogate

Tahir

et al. 2021

Entropy

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“…A. Data Set: SUST ALT (Sudan University of Science and Technology-Arabic Language Technology group) has built many datasets to be used in studying, investigating, training, and testing Arabic recognizers [7,8]. Isolated Arabic letters is one of these data set.…”

Section: Methodsmentioning

confidence: 99%

Convolutional Neural Network Model for Arabic Handwritten Characters Recognition

Elbashir¹,

Mustafa²

2018

International Journal of Advanced Research in Computer and Comm

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In this paper, we presented a Convolutional Neural Network (CNN) model for off-line Arabic handwritten character recognition. The proposed CNN model used the dataset which prepared by Sudan University of Science and Technology-Arabic Language Technology group. The dataset is pre-processed before feeding it to the CNN model. In the pre-processing, all the characters images are size normalized to fit in a 20 by 20 pixel and then centred in a scaled images of size 28×28 pixel using the centre of mass then all the images are converted to be having a black background and white foreground colours. The pre-processed images are fed to the CNN model, which is constructed using the sequential model of the Keras library under tensorflow environment. The accuracy obtained varied from 93.5% as test accuracy to 97.5% as training accuracy showing better results than other methods that used the same dataset.

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Local features enhancement using deep auto-encoder scheme for the recognition of the proposed handwritten Arabic-Maghrebi characters database

Djaghbellou

Attia

Bouziane

et al. 2022

Multimed Tools Appl

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Arabic handwritten datasets for pattern recognition and machine learning

Cited by 6 publications

References 3 publications

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Convolutional Neural Network Model for Arabic Handwritten Characters Recognition

Local features enhancement using deep auto-encoder scheme for the recognition of the proposed handwritten Arabic-Maghrebi characters database

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