Recognition of Handwritten Arabic Characters using Histograms of Oriented Gradient (HOG)

Jebril, Noor A.; Al-Zoubi, Hussein R.; Al‐Haija, Qasem Abu

doi:10.1134/s1054661818020141

Cited by 32 publications

(13 citation statements)

References 18 publications

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“…An accuracy of 98.86% was achieved when the strategy of DCNN using transfer learning was applied to two datasets. In another similar study [176] an OCR technique based on HOG (Histograms of Oriented Gradient) [177] for feature extraction and SVM for character classifi-cation was used on the handwritten dataset. The dataset contained names of Jordanian cities, towns and villages yielded an accuracy of 99%.…”

Section: E Arabic Scriptmentioning

confidence: 99%

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

240

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Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

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Section: E Arabic Scriptmentioning

confidence: 99%

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

240

View full text Add to dashboard Cite

show abstract

“…The segmentationbased approach, also called the analytical approach, decomposes the text into smaller units or primary and secondary strokes (characters or pseudo-characters), in a first phase. In a second phase, it classifies the units resulting from segmentation using a classifier like multilayer perceptron (MLP) [27] or support vector machine [28] and then combines them successively providing the recognized text. However, the effectiveness of these approaches relies heavily on the results of breaking down of text into units.…”

Section: ) Classificationmentioning

confidence: 99%

Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

et al. 2021

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One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddressed research field, because of several factors. Complications arise due to the cursive nature of the Arabic writing, character similarities, unlimited vocabulary, use of multi-size and mixed-fonts, etc. To handle these challenges, an automatic Arabic text recognition requires building a robust system by computing discriminative features and applying a rigorous classifier together to achieve an improved performance. In this work, we introduce a new deep learning based system that recognizes Arabic text contained in images. We propose a novel hybrid network, combining a Bag-of-Feature (BoF) framework for feature extraction based on a deep Sparse Auto-Encoder (SAE), and Hidden Markov Models (HMMs), for sequence recognition. Our proposed system, termed BoF-deep SAE-HMM, is tested on four datasets, namely the printed Arabic line images Printed KHATT (P-KHATT), the benchmark printed word images Arabic Printed Text Image (APTI), the benchmark handwritten Arabic word images IFN/ENIT, and the benchmark handwritten digits images Modified National Institute of Standards and Technology (MNIST).

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“…[51], three expressions are added to the Hu moment as the feature of gesture recognition to match the gesture template. In [52], when the static gesture feature is selected, the concave and the perimeter area ratio of the gesture contour and the first four Hu moments are combined, and the radial kernel function is used for SVM classification.In [53], HOG features are used to identify multiple gestures using the SVM classifier. Compared with the results in the above literature, the results of the maximum recognition rate, the minimum recognition rate and the average recognition rate are compared.…”

Section: Fig12 Recognition Rate On Different Featuresmentioning

confidence: 99%

Research on gesture recognition of smart data fusion features in the IoT

Tan

Sun

et al. 2019

Neural Comput & Applic

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With the rapid development of Internet of Things (IoT) technology, the interaction between people and things has become increasingly frequent. Use simple gestures instead of complex operations to interact with the machine, the fusion of smart data feature information and so on has gradually become a research hotspot. Considering that the depth image of the Kinect sensor lacks color information and is susceptible to depth thresholds, this paper proposes a gesture segmentation method based on the fusion of color information and depth information, in order to ensure the complete information of the segmentation image, a gesture feature extraction method based on Hu invariant moment and HOG feature fusion is proposed, by determining the optimal weight parameters, the global and local features are effectively fused. Finally, the SVM classifier is used to classify and identify gestures. The experimental results show that the proposed fusion features method has a higher gesture recognition rate and better robustness than the traditional method.

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Recognition of Handwritten Arabic Characters using Histograms of Oriented Gradient (HOG)

Cited by 32 publications

References 18 publications

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

Research on gesture recognition of smart data fusion features in the IoT

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