Investigation on deep learning for off-line handwritten Arabic character recognition

Boufenar, Chaouki; Kerboua, Adlen; Batouche, Mohamed

doi:10.1016/j.cogsys.2017.11.002

Cited by 110 publications

(67 citation statements)

References 10 publications

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“…Finally, CNN can be used via transfer learning by keeping the convolutional base in its original form and then using its outputs to feed the classifier. The pre-trained model is used as a fixed feature extraction mechanism in cases where the dataset is small, or when the problem is similar to the one to be classified [14].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network-Based Approaches for Face Recognition

2019

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Face recognition (FR) is defined as the process through which people are identified using facial images. This technology is applied broadly in biometrics, security information, accessing controlled areas, keeping of the law by different enforcement bodies, smart cards, and surveillance technology. The facial recognition system is built using two steps. The first step is a process through which the facial features are picked up or extracted, and the second step is pattern classification. Deep learning, specifically the convolutional neural network (CNN), has recently made commendable progress in FR technology. This paper investigates the performance of the pre-trained CNN with multi-class support vector machine (SVM) classifier and the performance of transfer learning using the AlexNet model to perform classification. The study considers CNN architecture, which has so far recorded the best outcome in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in the past years, more specifically, AlexNet and ResNet-50. In order to determine performance optimization of the CNN algorithm, recognition accuracy was used as a determinant. Improved classification rates were seen in the comprehensive experiments that were completed on the various datasets of ORL, GTAV face, Georgia Tech face, labelled faces in the wild (LFW), frontalized labeled faces in the wild (F_LFW), YouTube face, and FEI faces. The result showed that our model achieved a higher accuracy compared to most of the state-of-the-art models. An accuracy range of 94% to 100% for models with all databases was obtained. Also, this was obtained with an improvement in recognition accuracy up to 39%.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network-Based Approaches for Face Recognition

2019

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“…A recent study by Chaouki Boufenar et al [ 31 ], investigated the use of convolutional neural networks for offline Arabic handwritten character recognition. Their architecture consisted of five layers in which three convolutional layers with a max pool were connected to two fully connected layers.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition of Pashto Handwritten Characters Based on Deep Learning

Amin

Yasir

Ahn

2020

Sensors

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Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.

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“…There are two techniques to build a classifier model through the ConvNet algorithm. The first technique is the initial learning technique [23], [24]. This technique creates all-new architecture as well as initial learning the data from zero at the model learning state.…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

A Comparative Study of Learning Techniques with Convolutional Neural Network Based on HPC-Workload Dataset

Banjongkan¹,

Pongsena²,

Kerdprasop³

et al. 2020

IJMLC

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High-Performance Computing or HPC is a computer system that has high computing power. The HPC supports various computational domains. A huge amount of jobs from a large group of users prefer to complete their jobs in this kind of system. Therefore, managing the jobs or job scheduling is very important since it involves the overall system efficiency. The analysis of an HPC-workload log file is a solution to improve system efficiency. Because some information may appear in the log file, this information can help the system scheduler to make an appropriate decision for job scheduling in the HPC system. This research proposed predictive models for predicting the job status at the finishing state in the HPC system. The model can be used as a tool for monitoring the jobs in the HPC system. We develop and build the three models including HPC-CNN, HPC-AlexNet, and HPC-VGG16 based on the two different learning techniques, which comprise Initial and Transfer Learning of Convolutional Neural Network based on the HPC-work load dataset. Moreover, the three state-of-the-art Machine Learning methods: Classification and Regression Tree (CART), Artificial Neural Network (ANN), and Support Vector Machine (SVM) are used as the baseline models for performance comparison. The results show that the model that performs the best predictive performance is the proposed HPC-CNN model. It archives 76.48% accuracy of the prediction followed with the CART model (75.60%), while the SVM model performs lowest the accuracy at 66.80%.

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Investigation on deep learning for off-line handwritten Arabic character recognition

Cited by 110 publications

References 10 publications

Deep Convolutional Neural Network-Based Approaches for Face Recognition

Deep Convolutional Neural Network-Based Approaches for Face Recognition

Recognition of Pashto Handwritten Characters Based on Deep Learning

A Comparative Study of Learning Techniques with Convolutional Neural Network Based on HPC-Workload Dataset

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