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DOI: 10.14569/IJACSA.2018.090157
PDF

Comparative Analysis of Raw Images and Meta Feature based Urdu OCR using CNN and LSTM

Author 1: Asma Naseer
Author 2: Kashif Zafar

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 1, 2018.

  • Abstract and Keywords
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Abstract: Urdu language uses cursive script which results in connected characters constituting ligatures. For identifying characters within ligatures of different scales (font sizes), Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) Network are used. Both network models are trained on formerly extracted ligature thickness graphs, from which models extract Meta features. These thickness graphs provide consistent information across different font sizes. LSTM and CNN are also trained on raw images to compare performance on both forms of inputs. For this research, two corpora, i.e. Urdu Printed Text Images (UPTI) and Centre for Language Engineering (CLE) Text Images are used. Overall performance of networks ranges between 90% and 99.8%. Average accuracy on Meta features is 98.08% while using raw images, 97.07% average accuracy is achieved.

Keywords: Long Short Term Memory (LSTM); Convolution Neural Network (CNN); OCR; scale invariance; deep learning; ligature

Asma Naseer and Kashif Zafar, “Comparative Analysis of Raw Images and Meta Feature based Urdu OCR using CNN and LSTM” International Journal of Advanced Computer Science and Applications(IJACSA), 9(1), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090157

@article{Naseer2018,
title = {Comparative Analysis of Raw Images and Meta Feature based Urdu OCR using CNN and LSTM},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090157},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090157},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {1},
author = {Asma Naseer and Kashif Zafar}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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