Arabic Handwritten Character Recognition Based on Convolution Neural Networks

Bouchriha, Lamia; Zrigui, Ahmed; Mansouri, Sadek; Berchech, Salma; Omrani, Syrine

doi:10.1007/978-3-031-16210-7_23

Cited by 8 publications

(10 citation statements)

References 16 publications

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“…The comparison was made in terms of the target task, the methods used for feature extraction and classification, suggested supplementary (handcrafted) features, applied feature fusion technique, and the dataset used for training and testing the model. In these studies [ 4 , 16 , 17 , 20 ], researchers developed a new character recognition system using a CNN deep learning model, trained it, and tested it on children’s data samples. In [ 18 ], they designed a hybrid model by combining existing CNN models as feature extractors with SVM and XGBoost machine learning models as classifiers, which were trained and evaluated using the Hijja dataset.…”

Section: Discussion and Comparisonmentioning

confidence: 99%

“…We set the following hyperparameters for the SVM classifier: C = (1, 10, 100, 1000), kernel = ['poly', 'sigmoid', 'rbf'], and gamma = (0.01, 0.001, 0.0001), whereas for the KNN classifier we set the following: k = (5, 7, 9, 11), weights = ['distance'], and metric = ['Euclidean', 'Manhattan']. We also set the hyperparameters for the RF classifier as n_estimators = (50, 100, 200, 300, 400) and max_depth = (5,10,15,20,25,30). We tuned the hyperparameters of these classifiers using the grid search method and then determined the optimal hyperparameters that provide the highest possible classification accuracy.…”

Section: Hyperparameters Tuning and Data Augmentationmentioning

confidence: 99%

“…Their results showed that using the Nadam optimizer together with rotation and shifting data augmentation techniques gave their highest test accuracy of 98.32% among other choices when mixed with 80% of AHCD and 20% of Hijja for training along with 20% of AHCD and 10% of Hijja for testing. Bouchriha et al [ 20 ] also presented a novel CNN model for recognizing handwritten Arabic characters. They focused on unique characteristics of Arabic text, particularly the difference in the shape of letters according to their location in the word, and by using the Hijja dataset, they attained an accuracy of 95%.…”

Section: Related Workmentioning

confidence: 99%

“…The majority of research on handwritten Arabic character recognition has focused on adult handwriting, as the findings have revealed the effectiveness of their systems in achieving accuracy rates of up to 99% using deep learning and machine learning techniques [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Furthermore, a few researchers have recently focused on children’s handwriting data for recognizing Arabic letters due to its great significance for many applications and different purposes [ 4 , 16 , 17 , 18 , 19 , 20 ]. Employing character recognition capabilities in child-related applications such as education [ 21 ], interactive learning, physical or mental health assessment, or other possible practical purposes is critical for many future research areas.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

Alwagdani,

Jaha

2023

Sensors

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Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults’ handwriting, children’s handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children’s handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children’s handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%.

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Section: Discussion and Comparisonmentioning

confidence: 99%

Section: Hyperparameters Tuning and Data Augmentationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

Alwagdani,

Jaha

2023

Sensors

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“…Bouchriha at el. [22]Developed a model using a convolutional neural network (CNN) and a technique using deep learning (DL) for the identification of Arabic characters. Using a new model for the CNN network, they were able to deal with the particular aspects of Arabic text, in particular, the variation in character shape that is based on where in the world the character is located.…”

Section: Motivationmentioning

confidence: 99%

Arabic Handwritten Character Recognition Using Convolutional Neural Networks

Alsayed,

Li,

Fat’hAlalim

et al. 2023

Preprint

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The Arabic language is one of the six most important languages in the world. Because more than 420 million people worldwide use the Arabic script, research into the recognition of Arabic handwriting is crucial. The demand for software that can automatically read and interpret Arabic Handwriting has been rapidly expanding in recent years as the use of digital devices has become increasingly widespread. Characters are written by Hands in Arabic are more difficult to decipher than those noted in English or other languages because of the nature of the words used in Arabic. In this study, we designed a new model, Convolutional Neural Network 14 Layers (CNN-14), to recog-nise handwritten Arabic characters. The The model was trained and tested on the Arabic Handwritten Character Dataset (AHCD) and Hijja datasets, The proposed model achieved good results, with an accuracy of 99.36 per cent in AHCD and 94.35 per cent Hijja dataset.

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