Word-based Arabic handwritten recognition using SVM classifier with a reject option

Qacimy, Bouchra El; Kerroum, Mounir Ait; Hammouch, Ahmed

doi:10.1109/isda.2015.7489190

Cited by 10 publications

(12 citation statements)

References 29 publications

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“…[ 14 , 94 ]. One or more pre-processing techniques can be used based on the degree of the text image quality [ 1 , 95 ] and according to the targeted OAHR design.…”

Section: Oahr General Frameworkmentioning

confidence: 99%

“…In the field of handwriting recognition systems (HRSs), digits, characters, and word recognition systems are used in a variety of applications, including bank cheque processing [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ], office automation [ 9 , 10 , 11 , 12 ], document processing [ 3 ], document content-based retrieval [ 13 ], signature verification [ 4 , 7 ], postal code recognition [ 1 , 2 , 4 , 5 , 6 ] and digital character identification systems. HRS can be carried out both online and offline.…”

Section: Introductionmentioning

confidence: 99%

“…Building a novel robust offline automated HRS for Arabic handwritten scripts remains an open challenge. This is mainly due to several characteristics of the Arabic language, such as its cursive nature, ligatures, overlapping, and diacritical marks [ 1 , 3 , 7 , 22 , 23 , 24 , 25 ]. A Few letters in Arabic also have loops, and half of the letters have dots that are used to distinguish between characters.…”

Section: Introductionmentioning

confidence: 99%

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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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“…[ 14 , 94 ]. One or more pre-processing techniques can be used based on the degree of the text image quality [ 1 , 95 ] and according to the targeted OAHR design.…”

Section: Oahr General Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ahmed

Gogate

Tahir

et al. 2021

Entropy

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“…Additionally, many Arabic characters are utilized in numerous languages such the Iranian, Jawi, and Urdu languages [1,7,3]. Review of the literature uncovers that, so far, there are two major systems for offline Arabic text recognition; segmentation-free systems (holistic recognition approaches) and segmentation-based systems [25,12]. In the former systems, recognition is applied on the entire representation of the text or word, which is treated as one unit with no segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…These characteristics may be categorized into three classes: (i) high-level characteristics, which are drawn from the entire image of the text or word, (ii) medium-level characteristics that are derived from the characters, and (iii) low-level characteristics, which are usually extracted from the related sub-characters [18]. In other respects, the handwritten Arabic text may be recognized using various classifiers like the Support Vector Machines (SVM), Hidden Markov Model (HMM), the k-nearest neighbors (kNN), and the Artificial Neural Network (ANN) classifiers [11,12,26].…”

Section: Introductionmentioning

confidence: 99%

Handwritten Arabic Text Recognition using Principal Component Analysis and Support Vector Machines

Al-Saqqar¹,

AL-Shatnawi²,

Al‐diabat³

et al. 2019

IJACSA

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In this paper, an offline holistic handwritten Arabic text recognition system based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifiers is proposed. The proposed system consists of three primary stages: preliminary processing, feature extraction using PCA, and classification using the polynomial, linear, and Gaussian SVM classifiers. In this proposed system, text skeleton is first extracted and the images of the text are normalized into uniform size for extraction of the global features of the Arabic words using PCA. Recognition performance of this proposed system was evaluated on version 2 of the IFN/ENIT database of handwritten Arabic text using the polynomial, linear, and Gaussian SVM classifiers. The classification results of the proposed system were compared with the results produced by a benchmark. TRS that is depending on the Discrete Cosine Transform (DCT) method using numerous normalization sizes of Arabic text images. The experimental testing results support the effectiveness of the proposed system in holistic recognition of the handwritten Arabic text.

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Bangla Handwritten Digit Recognition and Generation

Sikder

2019

Proceedings of International Joint Conference on Computational Intelligence

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Word-based Arabic handwritten recognition using SVM classifier with a reject option

Cited by 10 publications

References 29 publications

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

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

Handwritten Arabic Text Recognition using Principal Component Analysis and Support Vector Machines

Bangla Handwritten Digit Recognition and Generation

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