Spatial Distribution of Ink at Keypoints (SDIK): A Novel Feature for Word Spotting in Arabic Documents

2021 ◽  
pp. 2250035
Author(s):  
Hamza Ghilas ◽  
Meriem Gagaoua ◽  
Abdelkamel Tari ◽  
Mohamed Cheriet
Keyword(s):  
Spatial Distribution ◽  
Branch Points ◽  
Word Spotting ◽  
Ibn Sina ◽  
Handwritten Documents ◽  
Matching Mechanism ◽  
Query Word

This paper addresses the challenging task of word spotting in Arabic handwritten documents. We proposed a novel feature that we called Spatial Distribution of Ink at Keypoints (SDIK). The proposed feature captures the characteristics of Arabic handwriting concentrated at endpoints and branch points. SDIK feature quantizes the spatial repartition of ink pixels in the neighborhoods of keypoints. The resulting SDIK features are very fast to match, we take this advantage to match a query word with lines images rather than words images. By this matching mechanism, we overcome the hard task of segmenting an Arabic document into words. The method proposed in this study is tested on historical Arabic document with IBN SINA dataset and on modern handwriting with IFN/ENIT database. The obtained results are great of interest for retrieving query words in an Arabic document.

Statistical script independent word spotting in offline handwritten documents

2014 ◽  
Vol 47 (3) ◽  
pp. 1039-1050 ◽  
Author(s):  
Safwan Wshah ◽  
Gaurav Kumar ◽  
Venu Govindaraju
Keyword(s):  
Word Spotting ◽  
Handwritten Documents

A Line-Oriented Approach to Word Spotting in Handwritten Documents

2000 ◽  
Vol 3 (2) ◽  
pp. 153-168 ◽  
Author(s):  
A. Kolcz ◽  
J. Alspector ◽  
M. Augusteijn ◽  
R. Carlson ◽  
G. Viorel Popescu
Keyword(s):  
Word Spotting ◽  
Handwritten Documents ◽  
Oriented Approach

scholarly journals A summary study on handwritten documents' word spotting

2021 ◽  
Vol 5 (2) ◽  
pp. 152
Author(s):  
Manal Boualam ◽  
Youssef Elfakir ◽  
Ghizlane Khaissidi ◽  
Mostafa Mrabti
Keyword(s):  
Word Spotting ◽  
Handwritten Documents

Scale Space Co-Occurrence HOG Features for Word Spotting in Handwritten Document Images

2016 ◽  
Vol 6 (2) ◽  
pp. 71-86 ◽  
Author(s):  
C. Thontadari ◽  
C. J. Prabhakar
Keyword(s):  
Spatial Information ◽  
Scale Parameter ◽  
Poor Performance ◽  
Scale Space ◽  
Feature Descriptor ◽  
Word Spotting ◽  
Handwritten Documents ◽  
Histograms Of Oriented Gradients ◽  
The Poor ◽  
Handwritten Document

In this paper, the authors proposed a Scale Space Co-occurrence Histograms of Oriented Gradients method (SS Co-HOG) for retrieving words from digitized handwritten documents. The poor performance of HOG based word spotting in handwritten documents is due to that HOG ignores spatial information of neighboring pixels whereas Co-HOG captures the spatial information of neighboring pixels through counting the occurrence of the gradient orientations of two or more neighboring pixels. The authors employed three scale parameter representation of an image and at each scale, they divide the word image into blocks and Co-HOG features are extracted from each block and finally concatenate them into form a feature descriptor. The proposed method is evaluated using precision and recall metrics through experimentation conducted on popular datasets such as IAM and GW and confirmed that their method outperforms for both the datasets.

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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