Offline Handwritten Quranic Text Recognition: A Research Perspective

Iqbal, Arshad; Zafar, Aasim

doi:10.1109/aicai.2019.8701404

Cited by 9 publications

(9 citation statements)

References 4 publications

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“…The pre-processing task can be achieved by applying many techniques that can deal with skew/slant detection and correction, thinning, binarization, noise removal, thresholding, normalization [ 3 , 92 , 93 ], resizing and compression, etc. [ 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%

“…Feature extraction is the process of extracting the most relevant and non-redundant attributes from the text image raw data [ 1 , 3 , 14 , 92 ] and then converting them into a vector of features [ 9 , 91 , 95 ]. The different types of feature methods are classified as structural features, statistical features, and feature space transformations [ 8 , 94 , 95 ]. Structural features describe the geometrical and topological characteristics of a text image pattern, such as directions, strokes, endpoints, the intersection of line segments, number of diacritical marks, loops, types of dots, zigzag, arcs, concavities, etc.…”

Section: Oahr General Frameworkmentioning

confidence: 99%

“…Structural features describe the geometrical and topological characteristics of a text image pattern, such as directions, strokes, endpoints, the intersection of line segments, number of diacritical marks, loops, types of dots, zigzag, arcs, concavities, etc. Statistical features are extracted from the pixels’ statistical distribution, and they describe the set of characteristic measurements of a text image pattern, such as number of loops, segments, branching and crossing points, and diacritics and their positions [ 95 ], and they have low complexity and high speed [ 1 , 3 , 8 , 94 ]. Feature space transformations convert the pixel representation to a more compact form with less feature vector dimensionality [ 7 , 8 , 94 ] among the methods involving principal component analysis (PCA), Walsh Hadamard Transform, Fourier Transform, Wavelets, Hough Transform, Gabor Transform, Rapid Transform, and Karhunen Loeve Expression and moments [ 1 , 3 , 8 , 9 , 94 , 95 ].…”

Section: Oahr General Frameworkmentioning

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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Section: Oahr General Frameworkmentioning

confidence: 99%

Section: Oahr General Frameworkmentioning

confidence: 99%

Section: Oahr General Frameworkmentioning

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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“…The basic database contains metadata describing the text written on the page, paragraph, and line levels [20][21][22]. This paper is structured as follows: The next section presents the characteristic of Arabic and English Digits.…”

Section: Introductionmentioning

confidence: 99%

A novel comprehensive database for arabic and english off-line handwritten digits recognition

Abdalkafor

Awad

Alheeti

2020

IJEECS

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<span>The recognition of Arabic handwritten is received at the same interest as other Latin languages. In Optical Character Recognition (OCR), handwriting Arabic recognition is considered as one of the critical and difficult tasks in the various scientific area. The main issues of this matter were due to the lack of public Arabic handwriting databases and the cursive nature of Arabic writing. In this paper, a new benchmark database is built for the Arabic and English off-line handwritten digits Recognition. The original form is divided into three groups: Arabic digits, English digits, and word Arabic digits which written five times by 100 different academic staff and students of university writers. Our database contains 14500 images; divided into two subsets of training and testing to help researchers through evaluating and comparing results obtained from their systems. </span>

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“…Handwritten text recognition is the ability of a system to recognize human handwritten and convert it into digital text, this study was first conducted by T.L Diamond in 1958 [1]. Recently, the study of handwritten text recognition has been growing rapidly, the huge number of these researches is proved by the number of its publication such as Sadkhan et al in 2018 who recognized handwritten based on ANN and wavelet Transformation [2], Iqbal and Zafar studied about Offline Handwritten Quranic Text Recognition in 2019 [3], Nguyen et al who recognized Japanese handwritten using Recurrent Neural Network in 2018 [4] and much more. Handwritten recognition is divided into two categories, namely on-line and off-line recognition.…”

Section: Introductionmentioning

confidence: 99%

K-Nearest Neighbor for Recognize Handwritten Arabic Character

Athoillah

2019

JMM

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Handwritten text recognition is the ability of a system to recognize human handwritten and convert it into digital text. Handwritten text recognition is a form of classification problem, so a classification algorithm such as Nearest Neighbor (NN) is needed to solve it. NN algorithms is a simple algorithm yet provide a good result. In contrast with other algorithms that usually determined by some hypothesis class, NN Algorithm finds out a label on any test point without searching for a predictor within some predefined class of functions. Arabic is one of the most important languages in the world. Recognizing Arabic character is very interesting research, not only it is a primary language that used in Islam but also because the number of this research is still far behind the number of recognizing handwritten Latin or Chinese research. Due to that's the background, this framework built a system to recognize handwritten Arabic Character from an image dataset using the NN algorithm. The result showed that the proposed method could recognize the characters very well confirmed by its average of precision, recall and accuracy.

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Offline Handwritten Quranic Text Recognition: A Research Perspective

Cited by 9 publications

References 4 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

A novel comprehensive database for arabic and english off-line handwritten digits recognition

K-Nearest Neighbor for Recognize Handwritten Arabic Character

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