A Comparative Study on Optical Modeling Units for Off-Line Arabic Text Recognition

2017 ◽  
Author(s):  
Mohammed Faouzi BenZeghiba
Keyword(s):  
Comparative Study ◽  
Text Recognition ◽  
Arabic Text ◽  
Optical Modeling

scholarly journals Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 18569-18584
Author(s):  
Najoua Rahal ◽  
Maroua Tounsi ◽  
Amir Hussain ◽  
Adel M. Alimi
Keyword(s):  
Text Recognition ◽  
Arabic Text

A NEW STRUCTURAL TECHNIQUE FOR RECOGNIZING PRINTED ARABIC TEXT

1995 ◽  
Vol 09 (01) ◽  
pp. 101-125 ◽  
Author(s):  
HUMOUD B. AL-SADOUN ◽  
ADNAN AMIN
Keyword(s):  
Binary Tree ◽  
Average Length ◽  
Recognition Rate ◽  
Text Recognition ◽  
Arabic Text ◽  
Tree Construction ◽  
Printed Text

This paper proposes a new structural technique for Arabic text recognition. The technique can be divided into five major steps: (1) preprocessing and binarization; (2) thinning; (3) binary tree construction; (4) segmentation; and (5) recognition. The advantage of this technique is that its execution does not depend on either the font or size of character. Thus, this same technique might be utilized for the recognition of machine or hand printed text. The relevant algorithm is implemented on a microcomputer. Experiments were conducted to verify the accuracy and the speed of this algorithm using about 20,000 subwords each with an average length of 3 characters. The subwords used were written using different fonts. The recognition rate obtained in the experiments indicated an accuracy of 93.38 % with a speed of 2.7 characters per second.

Multi‐dimensional long short‐term memory networks for artificial Arabic text recognition in news video

2018 ◽  
Vol 12 (5) ◽  
pp. 710-719 ◽  
Author(s):  
Oussama Zayene ◽  
Sameh Masmoudi Touj ◽  
Jean Hennebert ◽  
Rolf Ingold ◽  
Najoua Essoukri Ben Amara
Keyword(s):  
Short Term Memory ◽  
Text Recognition ◽  
Arabic Text ◽  
Short Term ◽  
Term Memory ◽  
News Video ◽  
Long Short Term Memory
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