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

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posted on 2019-01-01, 00:00 authored by Saad Bin Ahmed, Ibrahim A Hameed, Saeeda Naz, Imran Razzak, Rubiyah Yusof
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.

History

Journal

IEEE access

Volume

7

Pagination

153566-153578

Location

Piscataway, N.J.

Open access

  • Yes

ISSN

2169-3536

eISSN

2169-3536

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Publisher

IEEE

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