A Deep Learning Approach for Handwritten Arabic Names Recognition

Mustafa, Mohamed Elhafiz; Khalafallah, Murtada

doi:10.14569/ijacsa.2020.0110183

Cited by 11 publications

(9 citation statements)

References 13 publications

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“…The extracted features are classified using SVM. (Mustafa and Elbashir, 2020) proposed CNNs architecture for Arabic OCR. The architecture includes three Conv layers and two max-pooling layers.…”

Section: Related Workmentioning

confidence: 99%

A Printed Arabic Optical Character Recognition System using Deep Learning

Alghyaline¹

2022

Journal of Computer Science

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Recognizing Arabic script is challenging for many reasons: The Arabic language is cursive and morphologically rich. There is a high similarity between Arabic letters. Moreover, Arabic has many diacritics and dots, and they change the letter's phonetic transcription. This study proposes a Printed Arabic Optical Character Recognition approach (PAOCR) based on the state-of-the-art You Only Look Once (YOLO) object detector. Four techniques were proposed and implemented to design an end-to-end Arabic OCR system. First, the YOLO4 object detector is customized and trained on deep Convolutional Neural Networks (CNNs) to recognize Arabic characters. Second, the overlapped bounding boxes are processed to keep the most accurate box for each character. Third, the Hunspell library checks the word spelling and corrects the wrong ones. Fourth, edit distance is used to compare OCR misspelled words with Hunspell's suggestions and choose the closest correct word. The experimental results in the Arabic Printed Text Image (APTI) dataset showed that the Word Recognition Rate (WRR) of customized YOLO4 Arabic OCR is 66.7%, whereas applying the three proposed techniques with YOLO4 achieved 82.4%. A comparison with two existing OCR systems Tesseract and ABBYY showed that the proposed approach achieved a 95.7% Character Recognition Rate (CRR), whereas Tesseract and ABBYY achieved 92.3 and 84.8%, respectively.

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Section: Related Workmentioning

confidence: 99%

A Printed Arabic Optical Character Recognition System using Deep Learning

Alghyaline¹

2022

Journal of Computer Science

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“…Mustafa et al [ 90 ] presented a CNN model for recognizing SUST Arabic names (words) holistically. The authors employed the dropout and batch normalization techniques from the posted model’s structure, which helped their model be more suitable for solving the high-level dimensional problem.…”

Section: Related Workmentioning

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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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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“…They compared their approach using Alex net DCNN with a recognition rate of 95.6% with a dynamic Bayesian network (DBN) and SVM. Moreover, Mustafa et al [19] a deep learning approach is presented to classify and recognize Arabic names, and the accuracy reached 99.14% in the SUST-ARG-names dataset.…”

Section: Related Workmentioning

confidence: 99%

Arabic Handwritten Character Recognition based on Convolution Neural Networks and Support Vector Machine

Shams¹,

Elsonbaty²,

ElSawy³

2020

IJACSA

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Recognition of Arabic characters is essential for natural language processing and computer vision fields. The need to recognize and classify the handwritten Arabic letters and characters are essentially required. In this paper, we present an algorithm for recognizing Arabic letters and characters based on using deep convolution neural networks (DCNN) and support vector machine (SVM). This paper addresses the problem of recognizing the Arabic handwritten characters by determining the similarity between the input templates and the pre-stored templates using both fully connected DCNN and dropout SVM. Furthermore, this paper determines the correct classification rate (CRR) depends on the accuracy of the corrected classified templates, of the recognized handwritten Arabic characters. Moreover, we determine the error classification rate (ECR). The experimental results of this work indicate the ability of the proposed algorithm to recognize, identify, and verify the input handwritten Arabic characters. Furthermore, the proposed system determines similar Arabic characters using a clustering algorithm based on the K-means clustering approach to handle the problem of multi-stroke in Arabic characters. The comparative evaluation is stated and the system accuracy reached 95.07% CRR with 4.93% ECR compared with the state of the art.

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A Deep Learning Approach for Handwritten Arabic Names Recognition

Cited by 11 publications

References 13 publications

A Printed Arabic Optical Character Recognition System using Deep Learning

A Printed Arabic Optical Character Recognition System using Deep Learning

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

Arabic Handwritten Character Recognition based on Convolution Neural Networks and Support Vector Machine

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