IJSDR
IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

Issue: March 2024

Volume 9 | Issue 3

Impact factor: 8.15

Click Here For more Info

Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter

Copyright Infringement Claims
Indexing Partner
Published Paper Details
Paper Title: An Enhanced Approach Towards Object Detection Using Deep Learning Techniques
Authors Name: S.Priyadarsini , G. vijipriya , Dr. E.S.Shamila , Ms.A.Praveena , Ms.A.Rathika
Unique Id: IJSDR1811079
Published In: Volume 3 Issue 11, November-2018
Abstract: Text recognition in images and videos have advanced as an active research area over last few years. Text detection from the scene image is a process by which text regions are segmented from non-textual ones and they are organized in accordance with their correct direction of reading. Different text patterns and variant background interferences are the challenges that affect the consistency of text character extraction. Digitization of text documents is frequently united with the progress of optical character recognition (OCR). OCR tool gives good outcomes obtained to read the text from an image. The objective study of this paper is to propose a text recognition model that is designed using rich supervision to accelerate training and achieve state-of-the-art performance on several benchmark datasets. A deep neural network based system is used to read scene text and show that scene text reading can be effectively applied for the purpose of retrieving objects. In this paper, we examine a modest but powerful approach to make robust use of HOG and LBP features for text recognition. Histograms of Oriented Gradients (HOGs) and Local Binary Patterns (LBPs) have proven to be an effective descriptor for object recognition in general and text recognition in specific. Experimental results over the ICDAR 2013 and SVT 2010 dataset demonstrate the efficiency of the proposed approach.
Keywords: Key words: Deep learning, Image processing, ORC, HOG, LBP.
Cite Article: "An Enhanced Approach Towards Object Detection Using Deep Learning Techniques", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.3, Issue 11, page no.442 - 445, November-2018, Available :http://www.ijsdr.org/papers/IJSDR1811079.pdf
Downloads: 000336256
Publication Details: Published Paper ID: IJSDR1811079
Registration ID:180854
Published In: Volume 3 Issue 11, November-2018
DOI (Digital Object Identifier):
Page No: 442 - 445
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

Click Here to Download This Article

Article Preview

Click here for Article Preview







Major Indexing from www.ijsdr.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

Track Paper
Important Links
Conference Proposal
ISSN
DOI (A digital object identifier)


Providing A digital object identifier by DOI
How to GET DOI and Hard Copy Related
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Creative Commons License
This material is Open Knowledge
This material is Open Data
This material is Open Content
Social Media
IJSDR

Indexing Partner