NDLI: Urdu Nasta’liq text recognition system based on multi-dimensional recurrent neural network and statistical features

Content Provider	Springer Nature Link
Author	Naz, Saeeda Umar, Arif I. Ahmad, Riaz Ahmed, Saad B. Shirazi, Syed H. Razzak, Muhammad I.
Copyright Year	2015
Abstract	Character recognition for cursive script like Arabic, handwritten English and French is a challenging task which becomes more complicated for Urdu Nasta’liq text due to complexity of this script over Arabic. Recurrent neural network (RNN) has proved excellent performance for English, French as well as cursive Arabic script due to sequence learning property. Most of the recent approaches perform segmentation-based character recognition, whereas, due to the complexity of the Nasta’liq script, segmentation error is quite high as compared to Arabic Naskh script. RNN has provided promising results in such scenarios. In this paper, we achieved high accuracy for Urdu Nasta’liq using statistical features and multi-dimensional long short-term memory. We present a robust feature extraction approach that extracts feature based on right-to-left sliding window. Results showed that selected features significantly reduce the label error. For evaluation purposes, we have used Urdu printed text images dataset and compared the proposed approach with the recent work. The system provided 94.97 % recognition accuracy for unconstrained printed Nasta’liq text lines and outperforms the state-of-the-art results.
Starting Page	219
Ending Page	231
Page Count	13
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	28
Issue Number	2
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2015-09-16
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multi-dimensional recurrent neural network Long short-term memory OCR Urdu Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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