Arabic Online Handwriting Recognition (AOHR)

Al-Helali, Baligh; Mahmoud, Sabri A.

doi:10.1145/3060620

Cited by 34 publications

(19 citation statements)

References 94 publications

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“…Also, several letters have the same body (e.g., Baa ' ', Thaa ' ') but differ only in position and the number of dots and diacritical marks which may be accidentally misplaced in handwriting which emphasizes the fact that Arabic character is more difficult than many other scripts. Further detail of Arabic characteristics and difficulties of writing are available in [36].…”

Section: Language Specificationmentioning

confidence: 99%

Hybrid Architecture based on RNN-SVM for Multilingual Online Handwriting Recognition using Beta-elliptic and CNN models

Hamdi¹,

Boubaker²,

Rabhi³

et al. 2021

Preprint

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Currently, deep learning approaches have proven successful in the areas of handwriting recognition. Despite this, research in this field is still needed, especially in the context of multilingual online handwriting recognition scripts by adopting new network architectures and combining relevant parametric models. In this paper, we propose a multi-stage deep learning-based algorithm for multilingual online handwriting recognition based on hybrid deep Bidirectional Long Short Term Memory (DBLSTM) and SVM networks. The main contributions of our work lie in partly in the composition of a new multi-stage architecture of deep learning networks associated with effective feature vectors that integrate dynamic and visual characteristics. First, the proposed system proceeds by pretreating the acquired script and delimiting its Segments of Online Handwriting Trajectories (SOHTs). Second, two types of feature vectors combining BetaElliptic Model (BEM) and Convolutional Neural Network (CNN) are extracted for each SOHT in order to fuzzy classify them into k sub-groups using DBLSTM neural networks for both online and offline branches trained using an unsupervised fuzzy k-means algorithm. Finally, we combine the trained models to strengthen the discrimination power of the global system using SVM engine. Extensive experiments on three data sets were conducted to validate the performance of the proposed method. The experimental results show the effectiveness and complementarities of the individual modules and the advantage of their fusion.<br>

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Section: Language Specificationmentioning

confidence: 99%

Hybrid Architecture based on RNN-SVM for Multilingual Online Handwriting Recognition using Beta-elliptic and CNN models

Hamdi¹,

Boubaker²,

Rabhi³

et al. 2021

Preprint

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“…The classification phase has the responsibility for assigning a pattern into a pre-classified class based on the features of the pattern which have been extracted in the previous phase [18]. The pre-classified classes can be words, sub-words, characters or strokes, based on the OCR approach used [6]. There are a number of different classification approaches that have been applied for Arabic OCR, such as Hidden Markov Models (HMM), Support Vector Machines (SVM), K-nearest neighbour.…”

Section: F Classification Phasementioning

confidence: 99%

“…9, No. 9, 2018 416 | P a g e www.ijacsa.thesai.org There is no doubt that printed Arabic OCR faces a number of challenges and there is still an intensive need for more research [6]. However, most challenges facing the development of Arabic OCR are due to the characteristics of Arabic script.…”

Section: Introductionmentioning

confidence: 99%

Printed Arabic Script Recognition: A Survey

Alghamdi¹,

Teahan²

2018

ijacsa

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Optical character recognition (OCR) is essential in various real-world applications, such as digitizing learning resources to assist visually impaired people and transforming printed resources into electronic media. However, the development of OCR for printed Arabic script is a challenging task. These challenges are due to the specific characteristics of Arabic script. Therefore, different methods have been proposed for developing Arabic OCR systems, and this paper aims to provide a comprehensive review of these methods. This paper also discusses relevant issues of printed Arabic OCR including the challenges of printed Arabic script and performance evaluation. It concludes with a discussion of the current status of printed Arabic OCR, analyzing the remaining problems in the field of printed Arabic OCR and providing several directions for future research.

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“…Benchmark databases [ 2 , 5 , 8 , 13 , 14 ] for both online and offline HRSs, such as SUST-ALT and HACDB [ 19 ], are collected from digitized documents compiled by individuals using traditional writing instruments (pen or pencil) [ 5 ]. Other databases, such as ADAB and SUST-OLAH (characters and names) [ 20 ], are collected from written topics on digital instruments (i.e., smartphones or tablets) [ 5 , 21 ].…”

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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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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Arabic Online Handwriting Recognition (AOHR)

Cited by 34 publications

References 94 publications

Hybrid Architecture based on RNN-SVM for Multilingual Online Handwriting Recognition using Beta-elliptic and CNN models

Hybrid Architecture based on RNN-SVM for Multilingual Online Handwriting Recognition using Beta-elliptic and CNN models

Printed Arabic Script Recognition: A Survey

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

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