Urdu Nastaleeq Nib Calligraphy Pattern Recognition
American Journal of Neural Networks and Applications
Volume 6, Issue 2, December 2020, Pages: 16-21
Received: Mar. 31, 2020; Accepted: Apr. 10, 2020; Published: Aug. 27, 2020
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Authors
Mateen Ahmed Abbasi, Engineering and Information Technology, Khwaja Fareed University, Rahimyar Khan, Pakistan
Naila Fareen, Department of Computer Science, Allama Iqbal Open University, Islamabad, Pakistan
Adnan Ahmed Abbasi, Department of Management Science, Alhamd Islamic University, Islamabad, Pakistan
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Abstract
Nib calligraphy pattern recognition is the way to convert handwritten nib font into its equivalent machine understandable or readable form. Nib calligraphy pattern recognition is derived from pattern recognition and computer vision, a variety of work has been done on Urdu literature and on Urdu handwritten automatic line segmentation. This research work is based on Urdu Nastaleeq Nib calligraphy pattern recognition. The width of the Qalam (Nib) makes difficulties in recognition due to different width of qalam pattern varieties, so there is dire need to develop a system that can recognize the digitized image of Urdu Nastaleeq Nib font with high accuracy. The objective of this research is to create a ground for the development of an efficient and robust Urdu Optical Character Recognition (OCR) for Urdu Nastaleeq nib pattern recognition and to develop a system that can recognize the digitized image of Urdu Nastaleeq Nib font with high accuracy. Urdu Nastaleeq nib pattern recognition. The research work mainly focuses on identifying the Urdu nib calligraphy pattern recognition. The purpose of the research is to create a system for Urdu Nastaleeq Nib calligraphy pattern recognition to get benefit from the cultural heritage of Nib calligraphic material. The Urdu Nastaleeq Nib Calligraphy Pattern Recognition research work is proposed to be done on the calligraphic Urdu Nastaleeq Nib pattern recognition. This research mainly focuses on recognizing the handwritten Urdu Nastaleeq Nib typeset and eliminating the noise which is the main difficulty in interpretation the font clearly. The aim here is to build up a more consistent, correct and precise system for Urdu Nastaleeq Nib calligraphy Pattern Recognition.
Keywords
Nib Calligraphy, Optical Character Recognition, Pattern Recognition, Urdu Nastaleeq
To cite this article
Mateen Ahmed Abbasi, Naila Fareen, Adnan Ahmed Abbasi, Urdu Nastaleeq Nib Calligraphy Pattern Recognition, American Journal of Neural Networks and Applications. Vol. 6, No. 2, 2020, pp. 16-21. doi: 10.11648/j.ajnna.20200602.11
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Naz. s, Arif I. Umar R, Ahmad S. B. Ahmed, S. H. Shirazi, M. I. Razzak. 2017. Urdu Nasta’liq text recognition system based on multi-dimensional recurrent neural network and statistical features, Neural Computing and Applications, V. 28, pp 219-231.
[2]
Hasan, U. A., S. B. Ahmed, S. F. Rashid, F. Shafait and T. M. Breuel. 2013. Offline Printed Urdu Nastaleeq Script Recognition with Bidirectional LSTM Networks, 12th International Conference on Document Analysis and Recognition, pp. 1061-1065.
[3]
Sheikh, S. 2010. Arabic-Urdu script Recognition through Mouse: An Implementation using artificial neural Network, Seventh International Conference on Information Technology, pp. 307-310.
[4]
Steven M. B., Eric C. J. & David A. G. Retrieving OCR Text: A Survey of Current Approaches, 2003. ACM SIGIR, 36 (2), pp 58-61.
[5]
Atif G. &Shafiq R. Nastaleeq: a Challenge Accepted by Omega, Tugboat, 2007. XVII European TEX Conference, 29 (1), pp 89-94.
[6]
Zaheer A, J. k. Orakzai., I. Shamsher and A. Awais. 2007. Urdu Nastaleeq Optical Character Recognition, World Academy of Science, Engineering and Technology, pp. 249-252.
[7]
Haidar A., John S. G. &Hisham A. A Real-time DSP-Based Optical Character Recognition System for Isolated Arabic Character using the TI TMS320C6416T, 2008. Proceedings of the 2008 IAJC-IJME International Conference.
[8]
Tabassam, N., S. A. H. S. Naqvi, H. Rehman and F. Anoshia. 2009. Optical Character Recognition System for Urdu (Naskh Font) Using Pattern Matching Technique, International Journal of Image Processing, vol. 3, no. 3, pp. 99-104.
[9]
Hussain, S. A., S. Zaman and M. Ayub. 2009. A Self Organizing Map Based Urdu Nasakh Character Recognition, International Conference on Emerging Technologies (ICET), 19-20 Oct Islamabad, Pakistan, pp 267–273.
[10]
Zaheer A, Jehanzeb k. O., Inam S. IEEE International Conference on Computer Science and Information Technology, 2009. ICCSIT 2009 8-11 Aug. Beijing, China. pp 457–462.
[11]
Junaid T, Umar N, Muhammad U. N. Softconverter: A novel approach to construct OCR for printed Urdu isolated characters 2010. 2nd International Conference on Computer Engineering and Technology (ICCET), 2010 16-18 April. Chengdu, China. pp V3-495 - V3-498.
[12]
Shuwair S, Abdul W. 2010. Optical character recognition system for Urdu International Conference onInformation and Emerging Technologies (ICIET), 2010 14-16 June Karachi, Pakistan pp 1-5.
[13]
Sagheer, M. W., C. L. He., N. Nobile and C. Y. Suen. 2010. Holistic Urdu Handwritten Word Recognition Using Support Vector Machine, Proceedings of 20th International Conference on Pattern Recognition (ICPR), pp 1900-1903.
[14]
Bukhari, S. S. and T. M. Breuel. 2011. Generic Layout Analysis of Diverse Collection of Documents, International Conference on Document Analysis and Recognition (ICDAR), pp 1275–1279.
[15]
Ahsen R, Imran S, Ali A, Fahim A. 2012. An Unconstrained Benchmark Urdu Handwritten Sentence Database with Automatic Line Segmentation, International Conference on Frontiers in Handwriting Recognition (ICFHR), 201218-20 Sept Bari, Italy, pp 491–496.
[16]
M. I. Shah, J. Sadri, C. Y. Suen, and N. Nobile, "A New Multipurpose Comprehensive Database for Handwritten Dari Recognition," Eleventh International Conference on Frontiers in Handwriting Recognition, Montreal, Canada, August 2008 pp. 635-640.
[17]
S. Qasim, M. A Ismail, “Design and Implementation of Parallel SOM Model on GPGPU”, 5th International conference on computer science and information technology IEEE CSIT 2013, March 28-29, Amman, Jordan, pp 233–237.
[18]
Akram El-Korashy, Faisal Shafait Search space reduction for holistic ligature recognition in Urdu Nastaleeq script, 2013, ICDAR, 12th International Conference on Document Analysis and Recognition, pp 1125–1129.
[19]
Cheriet, M. N. Kharma, C. L. Liu and C. Y. Suen. 2007. Character Recognition Systems A Guide For Students And Practioners, Published By John Wiley & Sons.
[20]
S. Vavilis and E. Kavallieratou. 2011. A tool for tuning binarization techniques, International Conference on Document Analysis and Recognition, (ICDAR), pp. 1-5.
[21]
Yousefi, J. 2011. Image Binarization using Otsu Thresholding Algorithm, Image processing and digital image processing, International workshop on Document Analysis Systems, pp. 1-4.
[22]
Kohonen, T., J. Hynninen, J. Kangas, and J. Laaksonen. 1996. SOM PAK: The Self-Organizing Map program package, pp. 1-27.
[23]
Heaton, J. 2008. Introduction to Neural Networks for C#, Heaton Research, ISBN 1-60439-009-3.
[24]
Naz, S., K. Hayat, M. I. Razzak, M. W. Anwar and H. Akbar. 2013. Arabic script based character segmentation: A review, World Congress on Computer and Information Technology (WCCIT), pp. 1-6.
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