Deep ensemble neural networks for recognizing isolated Arabic handwritten characters

Alyahya, Haifa; Ismail, Mohamed Maher Ben; Al-Salman, AbdulMalik S.

doi:10.19101/tipcv.2020.618051

Cited by 22 publications

(13 citation statements)

References 22 publications

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“…Conceptualized heterogeneity is divided into scope heterogeneity and model coverage heterogeneity. Concept scope means that concepts with the same name often have different meanings in different fields; different modelers often divide concepts differently in the modeling process due to different domain requirements or subjective understandings [22][23][24]. Model coverage refers to the difference in the knowledge scope and level of detail described by different ontologies.…”

Section: Discussionmentioning

confidence: 99%

Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation

Zhu

2022

Occupational Therapy International

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Traditional English classroom teaching is difficult to meet the oral learning needs of most learners. Thanks to the continuous advancement of speech processing technology, computer-assisted language learning systems are becoming more intelligent, not only pointing out learners’ pronunciation errors but also assessing their overall pronunciation level. Method. This paper uses the method of tree kernel function to measure the similarity of two ontology trees. According to the features of nodes in ontology tree, methods to calculate the external features and internal features of nodes are proposed, respectively. External features are mainly obtained by calculating the hierarchical centrality, node density, and node coverage of nodes in the ontology tree; internal features are mainly obtained by measuring the richness of internal information. According to the similarity of ontology tree and the external features and internal features of nodes, the calculation formula of structural comprehensive similarity is improved, and the features of ontology itself can be fully considered in the calculation. According to the difference of the structure, the weights of the corresponding features during the calculation are adjusted autonomously, so that the calculation results are closer to reality. In spectral image preprocessing, endpoint detection utilizes the harmonic characteristics presented by narrowband spectrograms with high frequency resolution and eliminates useless nonspeech segments by detecting the presence of voiced segments. When building the neural network model, four convolutional layers, two fully connected layers, and one softmax output layer were conceived, and dropout was used to randomly suspend the work of some neurons to avoid overfitting. Results/Discussion. Through the data analysis of mean and variance and verified by one-way analysis of variance, it proves that the sentiment evaluation method in this paper is effective. The traditional multiple linear regression method is not suitable for the corpus and application scenarios of this paper. This paper proposes a decision tree structure, which is similar to the overall scoring process of raters, and uses the Interactive Dicremiser version 3 (ID3) algorithm to build a comprehensive evaluation decision tree for pitch, rhythm, intonation, speech rate, and emotion indicators. It is proved by experiments that the accurate consistency rate of the human-machine evaluation in this paper is 93%, the adjacent consistency rate is 96%, and the Pearson correlation coefficient value of the human-machine evaluation results is 0.89. The data results prove that the evaluation method in this paper is credible.

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Section: Discussionmentioning

confidence: 99%

Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation

Zhu

2022

Occupational Therapy International

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“…The proposed system attained maximum performance using the least volume of the training dataset. In literature [16], the researchers devised and validated a deep ensemble structure in which the ResNet18 structure was exploited for modelling and classification of the handwritten character images. Specifically, the researchers adapted the ResNet-18 model by totalling a dropout layer.…”

Section: Related Workmentioning

confidence: 99%

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

Maray¹,

Al-onazi²,

Alshahrani³

et al. 2023

Computers, Materials &Amp; Continua

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The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The projected SFODTL-AHCR model primarily focuses on identifying the handwritten Arabic characters in the input image. The proposed SFODTL-AHCR model pre-processes the input image by following the Histogram Equalization approach to attain this objective. The Inception with ResNet-v2 model examines the pre-processed image to produce the feature vectors. The Deep Wavelet Neural Network (DWNN) model is utilized to recognize the handwritten Arabic characters. At last, the SFO algorithm is utilized for fine-tuning the parameters involved in the DWNN model to attain better performance. The performance of the proposed SFODTL-AHCR model was validated using a series of images. Extensive comparative analyses were conducted. The proposed method achieved a 5468 CMC, 2023, vol.74, no.3 maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches.

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“…Alyahya et al in [15] investigated the performance of ResNet-18 architecture in recognizing Arabic handwritten characters when FCL and Dropout are added to the original architecture. They designed 4 deep models: 2 models that used a Fully Connected Layer with/without dropout layer after all convolutional layers and 2 models that used 2 Fully Connected Layers with/without a dropout layer.…”

Section: A Ahcr Cnns Modelsmentioning

confidence: 99%

A Hybrid Deep Model for Recognizing Arabic Handwritten Characters

Alrobah

Albahli

2021

IEEE Access

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Handwriting recognition for computer systems has been in research for a long time, with different researchers having an extensive variety of methods at their disposal. The problem is that most of these experiments are done inEnglish, as it is the most spoken language in the world. But other languages such as Arabic, Mandarin, Spanish, French, and Russian also need research done on them since there are millions of people who speak them. In this work, recognizing and developing Arabic handwritten characters is proposed by cleaning the state-of-the-art Arabic dataset called Hijaa, developing Conventional Neural Network (CNN) with a hybrid model using Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) classifiers. The CNN is used for feature extraction of the Arabic character images, which are then passed on to the Machine Learning classifiers. A recognition rate of up to 96.3% for 29classes is achieved, far surpassing the already state-of-the-art results of the Hijaa dataset.

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Deep ensemble neural networks for recognizing isolated Arabic handwritten characters

Cited by 22 publications

References 22 publications

Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation

Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

A Hybrid Deep Model for Recognizing Arabic Handwritten Characters

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