Segmentation and Word Spotting Methods for Printed and Handwritten Arabic Texts: A Comparative Study

2012 ◽  
Author(s):  
Mariem Gargouri Kchaou ◽  
Slim Kanoun ◽  
Jean-Marc Ogier
Keyword(s):  
Comparative Study ◽  
Word Spotting ◽  
Handwritten Arabic

Word spotting in handwritten Arabic documents using bag-of-descriptors

2016 ◽  
Vol 9 ◽  
pp. 1349-1357 ◽  
Author(s):  
Youssef Elfakir ◽  
Ghizlane Khaissidi ◽  
Mostafa Mrabti ◽  
Driss Chenouni ◽  
Mounim El Yacoubi
Keyword(s):  
Word Spotting ◽  
Handwritten Arabic

scholarly journals Segmentation-free Word Spotting for Handwritten Arabic Documents

2016 ◽  
Vol 4 (1) ◽  
pp. 6 ◽  
Author(s):  
Ghizlane Khaissidi ◽  
Youssef Elfakir ◽  
Mostafa Mrabti ◽  
Mounîm El Yacoubi ◽  
Driss Chenouni ◽  
...  
Keyword(s):  
Word Spotting ◽  
Free Word ◽  
Handwritten Arabic

Comparative study of conventional time series matching techniques for word spotting

2018 ◽  
Vol 73 ◽  
pp. 47-64 ◽  
Author(s):  
Tanmoy Mondal ◽  
Nicolas Ragot ◽  
Jean-yves Ramel ◽  
Umapada Pal
Keyword(s):  
Time Series ◽  
Comparative Study ◽  
Word Spotting ◽  
Matching Techniques

Word spotting for handwritten Arabic documents using Harris detector

2016 ◽  
Author(s):  
Youssef Elfakiri ◽  
Ghizlane Khaissidi ◽  
Mostafa Mrabti ◽  
Driss Chenouni ◽  
Mounim El Yacoubi
Keyword(s):  
Word Spotting ◽  
Harris Detector ◽  
Handwritten Arabic

scholarly journals The impact of the image processing in the indexation system

2019 ◽  
Vol 9 (5) ◽  
pp. 4311
Author(s):  
Youssef Elfakir ◽  
Ghizlane Khaissidi ◽  
Mostafa Mrabti ◽  
Driss Chenouni
Keyword(s):  
Sliding Window ◽  
Similarity Score ◽  
Local Region ◽  
Word Spotting ◽  
Processing Step ◽  
Codebook Size ◽  
Window Approach ◽  
The Impact ◽  
Processing Steps ◽  
Handwritten Arabic

This paper presents an efficient word spotting system applied to handwritten Arabic documents, where images are represented with bag-of-visual-SIFT descriptors and a sliding window approach is used to locate the regions that are most similar to the query by following the query-by-example paragon. First, a pre-processing step is used to produce a better representation of the most informative features. Secondly, a region-based framework is deployed to represent each local region by a bag-of-visual-SIFT descriptors. Afterward, some experiments are in order to demonstrate the codebook size influence on the efficiency of the system, by analyzing the curse of dimensionality curve. In the end, to measure the similarity score, a floating distance based on the descriptor’s number for each query is adopted. The experimental results prove the efficiency of the proposed processing steps in the word spotting system.

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