A Database for Arabic Handwritten Character Recognition

AlKhateeb, Jawad H.

doi:10.1016/j.procs.2015.09.130

Cited by 25 publications

(25 citation statements)

References 9 publications

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“…A database for Arabic characters is presented in [13] in which the authors performed pre-processing steps to avoid noise in the printed database. Another database for Arabic characters consists of 28 thousand characters of Arabic language written by 100 different writers [14]. A similar work has been reported by [14] as they target online recognition of Urdu characters collected from 100 writers for recognition of seven characters only.…”

Section: Literature Reviewmentioning

confidence: 95%

“…This review shows that most of the work done in the field of Urdu character recognition is for small datasets and with very limited generalization capability. Some progresses on Urdu script recognition are also presented in [13] and [14], but those are for printed text (typically popular with OCR applications) while we are developing an algorithm for handwritten Urdu text recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

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Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network

et al. 2020

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Automatic recognition of Urdu handwritten digits and characters, is a challenging task. It has applications in postal address reading, bank's cheque processing, and digitization and preservation of handwritten manuscripts from old ages. While there exists a significant work for automatic recognition of handwritten English characters and other major languages of the world, the work done for Urdu language is extremely insufficient. This paper has two goals. Firstly, we introduce a pioneer dataset for handwritten digits and characters of Urdu, containing samples from more than 900 individuals. Secondly, we report results for automatic recognition of handwritten digits and characters as achieved by using deep auto-encoder network and convolutional neural network. More specifically, we use a two-layer and a three-layer deep autoencoder network and convolutional neural network and evaluate the two frameworks in terms of recognition accuracy. The proposed framework of deep autoencoder can successfully recognize digits and characters with an accuracy of 97% for digits only, 81% for characters only and 82% for both digits and characters simultaneously. In comparison, the framework of convolutional neural network has accuracy of 96.7% for digits only, 86.5% for characters only and 82.7% for both digits and characters simultaneously. These frameworks can serve as baselines for future research on Urdu handwritten text.

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Section: Literature Reviewmentioning

confidence: 95%

Section: Literature Reviewmentioning

confidence: 99%

Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network

et al. 2020

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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%

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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“…The problem becomes more serious, when dealing with touching handwritten Arabic words because still there is a gap between human and machine abilities in reading handwriting text under noisy conditions especially for overlapped Arabic manuscripts. This due to the nature of font and style of the Arabic characters, which is written from right to left and is always cursive in both machine printed and handwritten text [7][8] [9]. Numerous attempts have been made for the recognition/segmentation of overlapped words in Arabic and other languages as well but these overlapped characters still exist gaps [10].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Touching Arabic Characters in Handwritten Documents by Overlapping Set Theory and Contour Tracing

Ullah¹,

Azmi²,

Ishak³

et al. 2019

IJACSA

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Segmentation of handwritten words into characters is one of the challenging problem in the field of OCR. In presence of touching characters, make this problem more difficult and challenging. There are many obstacles/challenges in segmentation of touching Arabic handwritten text. Although researches are busy in solving the problem of segmentation of these touching characters but still there exist unsolved problems of segmentation of touching offline Arabic handwritten characters. This is due to large variety of characters and their shapes. So in this research, a new method for segmentation of touching Arabic Handwritten character has been developed. The main idea of the proposed method is to segment the touching characters by identifying the touching point by overlapping set theory and ending points of the Arabic word by applying some standard morphology operation methods. After identifying all the points, segmentation method is applied to trace the boundaries of characters to separate these touching characters. Experiments were conducted on touching characters taken from different data sets. The results show the accuracy of the proposed method.

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A Database for Arabic Handwritten Character Recognition

Cited by 25 publications

References 9 publications

Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network

Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network

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

Segmentation of Touching Arabic Characters in Handwritten Documents by Overlapping Set Theory and Contour Tracing

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