Comparative systems of handwriting Arabic character recognition

Asebriy, Zahra; Raghay, Said; Bencharef, Omar; Chihab, Younes

doi:10.1109/icocs.2014.7060923

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…This step is vital, considering the importance of digitization in nowadays life. Handwriting recognition is employed in various domains such as document processing [25], writer identification, office automation, signature verification [5] automatic cheque processing in banks [26], postal code recognition [9], etc.…”

Section: Introductionmentioning

confidence: 99%

Attention-based deep learning model for Arabic handwritten text recognition

Gader

Echi

2022

MG&V

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This work proposes a segmentation-free approach to Arabic Handwritten Text Recognition (AHTR): an attention-based Convolutional Neural Network - Recurrent Neural Network - Connectionist Temporal Classification (CNN-RNN-CTC) deep learning architecture. The model receives as input an image and provides, through a CNN, a sequence of essential features, which are transferred to an Attention-based Bidirectional Long Short-Term Memory Network (BLSTM). The BLSTM gives features sequence in order, and the attention mechanism allows the selection of relevant information from the features sequences. The selected information is then fed to the CTC, enabling the loss calculation and the transcription prediction. The contribution lies in extending the CNN by dropout layers, batch normalization, and dropout regularization parameters to prevent over-fitting. The output of the RNN block is passed through an attention mechanism to utilize the most relevant parts of the input sequence in a flexible manner. This solution enhances previous methods by improving the CNN speed and performance and controlling over model over-fitting. The proposed system achieves the best accuracy of 97.1% for the IFN-ENIT Arabic script database, which competes with the current state-of-the-art. It was also tested for the modern English handwriting of the IAM database, and the Character Error Rate of 2.9% is attained, which confirms the model's script independence.

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Section: Introductionmentioning

confidence: 99%

Attention-based deep learning model for Arabic handwritten text recognition

Gader

Echi

2022

MG&V

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“…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%

“…HRS can be carried out both online and offline. Online applications of HRS use digital instruments [ 2 , 13 ], and the identification of characters is dynamically achieved in a sequential manner [ 13 ]. The detection also considers key factors, such as pen pressure and velocity [ 8 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…The detection also considers key factors, such as pen pressure and velocity [ 8 , 14 ]. Handwriting recognition of scanned documents and digital images is carried out offline [ 2 , 14 ]. Content in these images can be easily converted into editable character codes or words using an HRS, which makes it very useful for text processing applications [ 8 , 13 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…Offline HRS is more challenging and complex when compared to online HRS [ 8 , 14 ]. 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%

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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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