An Historical Handwritten Arabic Dataset for Segmentation-Free Word Spotting - HADARA80P

2014 ◽  
Author(s):  
Werner Pantke ◽  
Martin Dennhardt ◽  
Daniel Fecker ◽  
Volker Margner ◽  
Tim Fingscheidt
Keyword(s):  
Word Spotting ◽  
Free Word ◽  
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

A classification-free word-spotting system

2013 ◽  
Author(s):  
Nikos Vasilopoulos ◽  
Ergina Kavallieratou
Keyword(s):  
Word Spotting ◽  
Free Word

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

Segmentation Free Word Spotting for Handwritten Documents Using Bag of Visual Words Based on Co-HOG Descriptor

2019 ◽  
Vol 9 (2) ◽  
pp. 49-65
Author(s):  
Thontadari C. ◽  
Prabhakar C. J.
Keyword(s):  
Visual Information ◽  
Spatial Information ◽  
Spatial Location ◽  
Visual Word ◽  
Bag Of Visual Words ◽  
Word Spotting ◽  
Handwritten Documents ◽  
Visual Words ◽  
Handwritten Document ◽  
Free Word

In this article, the authors propose a segmentation-free word spotting in handwritten document images using a Bag of Visual Words (BoVW) framework based on the co-occurrence histogram of oriented gradient (Co-HOG) descriptor. Initially, the handwritten document is represented using visual word vectors which are obtained based on the frequency of occurrence of Co-HOG descriptor within local patches of the document. The visual word representation vector does not consider their spatial location and spatial information helps to determine a location exclusively with visual information when the different location can be perceived as the same. Hence, to add spatial distribution information of visual words into the unstructured BoVW framework, the authors adopted spatial pyramid matching (SPM) technique. The performance of the proposed method evaluated using popular datasets and it is confirmed that the authors' method outperforms existing segmentation free word spotting techniques.

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