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

An overview on handwritten documents word spotting

2019 ◽  
Author(s):  
Manal Boualam ◽  
Ghizlane Khaissidi ◽  
Mostafa Mrabti ◽  
Youssef Elfakir
Keyword(s):  
Word Spotting ◽  
Handwritten Documents

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

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.

HMM-based Word Spotting in Handwritten Documents Using Subword Models

2010 ◽  
Author(s):  
Andreas Fischer ◽  
Andreas Keller ◽  
Volkmar Frinken ◽  
Horst Bunke
Keyword(s):  
Word Spotting ◽  
Handwritten Documents
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