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