Openly accessible

Evaluation of handwritten Urdu text by integration of MNIST dataset learning experience

Ahmed, Saad Bin, Hameed, Ibrahim A., Naz, Saeeda, Razzak, Muhammad Imran and Yusof, Rubiyah 2019, Evaluation of handwritten Urdu text by integration of MNIST dataset learning experience, IEEE access, vol. 7, pp. 153566-153578, doi: 10.1109/access.2019.2946313.

Attached Files
Name Description MIMEType Size Downloads

Title Evaluation of handwritten Urdu text by integration of MNIST dataset learning experience
Author(s) Ahmed, Saad Bin
Hameed, Ibrahim A.
Naz, Saeeda
Razzak, Muhammad ImranORCID iD for Razzak, Muhammad Imran orcid.org/0000-0002-3930-6600
Yusof, Rubiyah
Journal name IEEE access
Volume number 7
Start page 153566
End page 153578
Total pages 13
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2019
ISSN 2169-3536
2169-3536
Summary The similar nature of patterns may enhance the learning if the experience they attained during training is utilized to achieve maximum accuracy. This paper presents a novel way to exploit the transfer learning experience of similar patterns on handwritten Urdu text analysis. The MNIST pre-trained network is employed by transferring it's learning experience on Urdu Nastaliq Handwritten Dataset (UNHD) samples. The convolutional neural network is used for feature extraction. The experiments were performed using deep multidimensional long short term (MDLSTM) memory networks. The obtained result shows immaculate performance on number of experiments distinguished on the basis of handwritten complexity. The result of demonstrated experiments show that pre-trained network outperforms on subsequent target networks which enable them to focus on a particular feature learning. The conducted experiments presented astonishingly good accuracy on UNHD dataset.
Language eng
DOI 10.1109/access.2019.2946313
Indigenous content off
HERDC Research category C1.1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30132739

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

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.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 104 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Mon, 02 Dec 2019, 13:17:22 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.