A Recognition-Based Approach to Segmenting Arabic Handwritten Text

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Journal of Intelligent Learning Systems and Applications

ISSN Print: 2150-8402
ISSN Online: 2150-8410
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"A Recognition-Based Approach to Segmenting Arabic Handwritten Text"
written by Ashraf Elnagar, Rahima Bentrcia,
published by Journal of Intelligent Learning Systems and Applications, Vol.7 No.4, 2015

has been cited by the following article(s):

Google Scholar
CrossRef

[1]	Hybrid Arabic handwritten character segmentation using CNN and graph theory algorithm
	… of King Saud University-Computer and …, 2024

[2]	A Hybrid Deep Learning Approach for Arabic Handwritten Recognition: Exploring the Complexities of the Arabic Language
	Available at SSRN …, 2023

[3]	A Deep Learning Approach to Recognize Mixed Fonts Printed Arabic Characters
	2022 5th International …, 2022

[4]	les travaux scientifique de Rahima Bentrcia
	2022

[5]	Publications du professeur BENTRCIA Rahima
	2022

[6]	Les Articles scientifique du professeur: Rahima Bentrcia
	2022

[7]	Publications of professor Rahima Bentrcia
	2022

[8]	An approach to analysis of Arabic text documents into text lines, words, and characters
	Indonesian Journal of …, 2022

[9]	Professor's scientific research: Rahima Bentrcia
	2022

[10]	The scientific work of: BENTRCIA Rahima
	2022

[11]	SCIENCES FACULTY SCIENTIFIC WORKS
	2021

[12]	Arabic handwritten recognition based on deep convolutional neural network
	2021

[13]	Ensemble learning using multi-objective optimisation for arabic handwritten words
	2021

[14]	TRAVAUX SCIENTIFIQUES DE LA FACULTE de mathématiques et de l'informatique
	2021

[15]	Advanced Stroke Labeling Technique Based on Directions Features for Arabic Character Segmentation
	2019

[16]	Automatic handwriter identification using advanced machine learning.
	2019

[17]	ADVANCED STROKE LABELLING TECHNIQUE BASED ON DIRECTIONS FEATURES FOR ARABIC CHARACTER SEGMENTATION
	2019

[18]	Automatic handwriter identification using advanced machine learning
	2019

[19]	Approach for Machine-Printed Arabic Character Recognition: the-state-of-the-art deep-learning method
	Electronic Imaging, 2018

[20]	Auto-Encoder-BoF/HMM System for Arabic Text Recognition
	2018

[21]	Lines segmentation and word extraction of Arabic handwritten text
	2018

[22]	Text Line and Word Extraction of Arabic Handwritten Documents
	2018

[23]	A Novel Method to Detect Segmentation points of Arabic Words using Peaks and Neural Network
	2017

[24]	On Arabic Character Recognition Employing Hybrid Neural Network
	International Journal of Advanced Computer Science and Applications, 2017

[25]	Segmentation Accuracy for Offline Arabic Handwritten Recognition Based on Bounding Box Algorithm
	International Journal of Computer Science and Network Security (IJCSNS), 2016

[26]	Graph Modeling based Segmentation of Handwritten Arabic Text into Constituent Sub-words
	2016

[27]	Graph Modeling based Segmentation of Handwritten Arabic Text into Constituent Subwords.
	2016

[1]	A Deep Learning Approach to Recognize Mixed Fonts Printed Arabic Characters 2022 5th International Symposium on Informatics and its Applications (ISIA), 2022 DOI:10.1109/ISIA55826.2022.9993489

[2]	A Deep Learning Approach to Recognize Mixed Fonts Printed Arabic Characters 2022 5th International Symposium on Informatics and its Applications (ISIA), 2022 DOI:10.1109/ISIA55826.2022.9993489

[3]	Transactions on Large-Scale Data- and Knowledge-Centered Systems XLI Lecture Notes in Computer Science, 2019 DOI:10.1007/978-3-030-11196-0_42

[4]	Lines segmentation and word extraction of Arabic handwritten text Proceedings of the 3rd International Conference on Smart City Applications, 2018 DOI:10.1145/3286606.3286831

[5]	Lines segmentation and word extraction of Arabic handwritten text Proceedings of the 3rd International Conference on Smart City Applications, 2018 DOI:10.1145/3286606.3286831

[6]	Lines segmentation and word extraction of Arabic handwritten text Proceedings of the 3rd International Conference on Smart City Applications, 2018 DOI:10.1145/3286606.3286831

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

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