Deep learning based isolated Arabic scene character recognition Ahmed, Saad Bin, Naz, Saeeda, Razzak, Muhammad Imran and Yousaf, Rubiyah 2017, Deep learning based isolated Arabic scene character recognition, in ASAR 2017 : 1st International Workshop on Arabic Script Analysis and Recognition, IEEE, Piscataway, N.J., pp. 46-51, doi: 10.1109/asar.2017.8067758. Attached Files Name Description MIMEType Size Downloads Title Deep learning based isolated Arabic scene character recognition Author(s) Ahmed, Saad Bin Naz, Saeeda Razzak, Muhammad Imran orcid.org/0000-0002-3930-6600 Yousaf, Rubiyah Conference name Arabic Script Analysis and Recognition. International Workshop ( 1st : 2017 : Nancy, France) Conference location Nancy, France Conference dates 2017/04/03 - 2017/04/05 Title of proceedings ASAR 2017 : 1st International Workshop on Arabic Script Analysis and Recognition Publication date 2017 Start page 46 End page 51 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. ISBN 9781509066285 Language eng DOI 10.1109/asar.2017.8067758 Indigenous content off HERDC Research category E1.1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30132751 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 02 Dec 2019, 14:20:02 EST Tue, 03 Dec 2019, 04:00:56 EST Tue, 03 Dec 2019, 05:05:43 EST Mon, 09 Dec 2019, 12:56:23 EST Mon, 09 Dec 2019, 12:58:29 EST Fri, 20 Dec 2019, 11:24:00 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 60 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Mon, 02 Dec 2019, 14:20:02 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.