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Urdu Nasta'liq text recognition using implicit segmentation based on multi-dimensional long short term memory neural

Saeeda Naz1, Arif Iqbal Umar2, Riaz Ahmed3

  • 1National University of Sciences and Technology (NUST), Islamabad, Pakistan ; Department of Information Technology, Hazara University, Mansehra, Pakistan.

Springerplus
|December 13, 2016
PubMed
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This study introduces a novel Multi-dimensional Long Short Term Memory (MDLSTM) Recurrent Neural Network for Urdu text recognition. The developed model achieves 98% accuracy for Nasta'liq script, outperforming existing methods.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Arabic script recognition is challenging due to its complexity.
  • Urdu text recognition, specifically, is hindered by the Nasta'liq style's calligraphic nature, including diagonality, cursiveness, context sensitivity, and character overlap.
  • Existing Arabic script recognition methods are not directly applicable to Urdu.

Purpose of the Study:

  • To develop an effective method for recognizing printed Urdu text-lines in the Nasta'liq writing style.
  • To address the specific challenges posed by the Nasta'liq script for automated text recognition.

Main Methods:

  • Implementation of Multi-dimensional Long Short Term Memory (MDLSTM) Recurrent Neural Networks.
  • Design of a specialized output layer for sequence labeling tailored for Urdu text recognition.
Keywords:
BLSTMCTCMDLSTMUrdu OCR

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Main Results:

  • Achieved a 98% recognition accuracy for unconstrained Urdu Nasta'liq printed text.
  • Demonstrated significant performance improvement over state-of-the-art techniques for Urdu text recognition.

Conclusions:

  • MDLSTM Recurrent Neural Networks are highly effective for Urdu Nasta'liq text recognition.
  • The proposed method offers a substantial advancement in accurately recognizing complex Urdu script, paving the way for improved digital processing of Urdu documents.