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Reseach Article

Study of Entity Detection and Identification using Deep Learning Techniques a Survey

by Abhishek Ratnaparkhi, Sushant Joshi, Rhushikesh Valiv
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 48
Year of Publication: 2020
Authors: Abhishek Ratnaparkhi, Sushant Joshi, Rhushikesh Valiv
10.5120/ijca2020919900

Abhishek Ratnaparkhi, Sushant Joshi, Rhushikesh Valiv . Study of Entity Detection and Identification using Deep Learning Techniques a Survey. International Journal of Computer Applications. 177, 48 ( Mar 2020), 20-24. DOI=10.5120/ijca2020919900

@article{ 10.5120/ijca2020919900,
author = { Abhishek Ratnaparkhi, Sushant Joshi, Rhushikesh Valiv },
title = { Study of Entity Detection and Identification using Deep Learning Techniques a Survey },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2020 },
volume = { 177 },
number = { 48 },
month = { Mar },
year = { 2020 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number48/31233-2020919900/ },
doi = { 10.5120/ijca2020919900 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:48:58.902856+05:30
%A Abhishek Ratnaparkhi
%A Sushant Joshi
%A Rhushikesh Valiv
%T Study of Entity Detection and Identification using Deep Learning Techniques a Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 48
%P 20-24
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Real-time object detection is a recent trend in image processing that plays a very important role in detection of objects and identifying them. Also there are various tools for image processing to identify objects. There are also frameworks which uses end to end network and shows very good results in object detection. However, compared to more accurate but time-consuming frameworks, detection accuracy of existing real-time networks are still left far behind. In this survey paper we have studied different object identification techniques. In this paper various frameworks like HyperNet, novel CAD YOLO Voxnet are studied. Various methods for object detection and identification like region generation, scale invariant detection, non maximum weighted, Sparse matrix distribution, Background modeling, Speed Up Robust Feature(SURF), Single Shot Detection(SSD) are also studied. R-CNN, Edge detection algorithms and Approach based studies are learned.

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Index Terms

Computer Science
Information Sciences

Keywords

CNN deep learning object detection object tracking object identification edge detection.