A graph-based segmentation and feature extraction framework for Arabic text recognition

2002 ◽  
Author(s):  
A.M. Elgammal ◽  
M.A. Ismail
Keyword(s):  
Feature Extraction ◽  
Text Recognition ◽  
Arabic Text

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.

scholarly journals Recognizing Cursive Typewritten Text Using Segmentation-Free System

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammad S. Khorsheed
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Feature Space ◽  
Recognition System ◽  
Image Feature ◽  
Text Recognition ◽  
Statistical Features ◽  
Free System ◽  
Novel Method ◽  
Image Pixels

Feature extraction plays an important role in text recognition as it aims to capture essential characteristics of the text image. Feature extraction algorithms widely range between robust and hard to extract features and noise sensitive and easy to extract features. Among those feature types are statistical features which are derived from the statistical distribution of the image pixels. This paper presents a novel method for feature extraction where simple statistical features are extracted from a one-pixel wide window that slides across the text line. The feature set is clustered in the feature space using vector quantization. The feature vector sequence is then injected to a classification engine for training and recognition purposes. The recognition system is applied to a data corpus which includes cursive Arabic text of more than 600 A4-size sheets typewritten in multiple computer-generated fonts. The system performance is compared to a previously published system from the literature with a similar engine but a different feature set.

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