Urdu Nasta’liq text recognition system based on multi-dimensional recurrent neural network and statistical features Naz, Saeeda, Umar, Arif I., Ahmad, Riaz, Ahmed, Saad B., Shirazi, Syed H. and Razzak, Muhammad I. 2017, Urdu Nasta’liq text recognition system based on multi-dimensional recurrent neural network and statistical features, Neural computing and applications, vol. 28, pp. 219-231, doi: 10.1007/s00521-015-2051-4. Attached Files Name Description MIMEType Size Downloads Title Urdu Nasta’liq text recognition system based on multi-dimensional recurrent neural network and statistical features Author(s) Naz, Saeeda Umar, Arif I. Ahmad, Riaz Ahmed, Saad B. Shirazi, Syed H. Razzak, Muhammad I. orcid.org/0000-0002-3930-6600 Journal name Neural computing and applications Volume number 28 Start page 219 End page 231 Total pages 13 Publisher Springer Place of publication London, Eng. Publication date 2017 ISSN 0941-0643 Keyword(s) Multi-dimensional recurrent neural network Long short-term memory OCR Urdu Language eng DOI 10.1007/s00521-015-2051-4 Indigenous content off Field of Research 0801 Artificial Intelligence and Image Processing 1702 Cognitive Sciences HERDC Research category C1.1 Refereed article in a scholarly journal Copyright notice ©2015, The Natural Computing Applications Forum Persistent URL http://hdl.handle.net/10536/DRO/DU:30132885 Document type: Journal Article 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 Thu, 05 Dec 2019, 11:03:44 EST Fri, 06 Dec 2019, 04:00:30 EST Fri, 06 Dec 2019, 05:04:04 EST Thu, 09 Jan 2020, 09:50:53 EST Thu, 09 Jan 2020, 09:53:13 EST Thu, 09 Jan 2020, 09:53:51 EST Sat, 09 Jan 2021, 01:37:22 EST Filtered Full Citation counts:   Cited 26 times in TR Web of Science Cited 32 times in Scopus Search Google Scholar Access Statistics: 104 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Thu, 05 Dec 2019, 11:03:44 EST

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