eprintid: 13862
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/38/62
datestamp: 2023-11-10 03:28:25
lastmod: 2023-11-10 03:28:25
status_changed: 2023-11-10 01:52:10
type: article
metadata_visibility: show
creators_name: Katper, S.H.
creators_name: Gilal, A.R.
creators_name: Waqas, A.
creators_name: Alshanqiti, A.
creators_name: Alsughayyir, A.
creators_name: Jaafar, J.
title: Deep neural networks nombined with STN for multi-oriented text detection and recognition
ispublished: pub
keywords: Character recognition; Network architecture, Benchmark datasets; Deep neural network; Document recognition; Documents analysis; International conference on document analyse and recognition dataset; Localisation; Multi-oriented text; Spatial transformer network; Spatial transformer network-OCR; Text region, Deep neural networks
note: cited By 9
abstract: Developing systems for interpreting visuals, such as images, videos is really challenging but important task to be developed and applied on benchmark datasets. This study solves the very challenge by using STN-OCR model consisting of deep neural networks (DNN) and Spatial Transformer Networks (STNs). The network architecture of this study consists of two stages: localization network and recognition network. In the localization network it finds and localizes text regions and generates sampling grid. Whereas, in the recognition network, text regions will be input and then this network learns to recognize text including low resolution, curved and multi-oriented text. Deep learning-based approaches require a lot of data for training effectively, therefore, this study has used two benchmark datasets, Street View House Numbers (SVHN) and International Conference on Document Analysis and Recognition (ICDAR) 2015 to evaluate the system. The STN-OCR model achieves better results than literature on these datasets. © 2020 Science and Information Organization.
date: 2020
publisher: Science and Information Organization
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085521236&doi=10.14569%2fIJACSA.2020.0110424&partnerID=40&md5=20229642338dccd786a3cf530a4f0869
id_number: 10.14569/IJACSA.2020.0110424
full_text_status: none
publication: International Journal of Advanced Computer Science and Applications
volume: 11
number: 4
pagerange: 178-184
refereed: TRUE
issn: 2158107X
citation:   Katper, S.H. and Gilal, A.R. and Waqas, A. and Alshanqiti, A. and Alsughayyir, A. and Jaafar, J.  (2020) Deep neural networks nombined with STN for multi-oriented text detection and recognition.  International Journal of Advanced Computer Science and Applications, 11 (4).  pp. 178-184.  ISSN 2158107X