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Thematische Bibliographien / Urdu Character Recognition

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Zeitschriftenartikel
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Konferenzberichte

Auswahl der wissenschaftlichen Literatur zum Thema „Urdu Character Recognition“

Autor: Grafiati

Veröffentlicht am 7. Juni 2025

Zuletzt aktualisiert am 2. August 2025

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Zeitschriftenartikel zum Thema "Urdu Character Recognition"

1

Siddiqui, Sayma Shafeeque A. W., Rajashri G. Kanke, Ramnath M. Gaikwad, and Manasi R. Baheti. "Review on Isolated Urdu Character Recognition: Offline Handwritten Approach." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 384–88. http://dx.doi.org/10.22214/ijraset.2023.55164.

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Abstract: This paper summarizes a system for recognizing isolated Urdu characters using advanced machine learning algorithms. The system analyzes visual features of Urdu characters, like strokes and curves, to train models such as CNN, SVM, ANN, and MLP. With a large dataset, the system can accurately predict unseen characters. It can be integrated into various applications for real-time character recognition tasks like OCR (Optical Character Recognition) and handwriting recognition. This literature survey explores research papers focused on character recognition in languages like Urdu, Arabic

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2

M, Ameen Chhajro, Khan Hadeeb, Khan Farrukh, Kumar Kamlesh, Ali Wagan Asif, and Solangi Sadaf. "Handwritten Urdu character recognition via images using different machine learning and deep learning techniques." Indian Journal of Science and Technology 13, no. 17 (2020): 1746–54. https://doi.org/10.17485/IJST/v13i17.113.

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Abstract <strong>Objectives:</strong> This research presents a model for Urdu Handwritten Character Recognition via images using various Machine Learning and Deep Learning Techniques. The main objective of this research is to provide comparative study on Urdu Handwritten Characters from images dataset. <strong>Methods/Statistical analysis:</strong> In this research paper, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) algorithm, Multi-Layer Perceptron (MLP), Concurrent Neural Network (CNN), Recurrent Neural Network (RNN) and Random Forest Algorithm (RF) have been implem

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3

Naaz Ansari, Nighat, and Ajay Kumar Singh. "Urdu Character Recognition using Neural Network." International Journal of Computer Trends and Technology 36, no. 4 (2016): 172–75. http://dx.doi.org/10.14445/22312803/ijctt-v36p131.

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4

Kazmi, M., F. Yasir, S. Habib, M. S. Hayat, and S. A. Qazi. "Photometric Ligature Extraction Technique for Urdu Optical Character Recognition." Engineering, Technology & Applied Science Research 11, no. 6 (2021): 7968–73. http://dx.doi.org/10.48084/etasr.4596.

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Urdu Optical Character Recognition (OCR) based on character level recognition (analytical approach) is less popular as compared to ligature level recognition (holistic approach) due to its added complexity, characters and strokes overlapping. This paper presents a holistic approach Urdu ligature extraction technique. The proposed Photometric Ligature Extraction (PLE) technique is independent of font size and column layout and is capable to handle non-overlapping and all inter and intra overlapping ligatures. It uses a customized photometric filter along with the application of X-shearing and p

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5

Muhammad, Murad, Shahzad Muhammad, and Fareed Naheeda. "Research Comparative Analysis of OCR Models for Urdu Language Characters Recognition." LC International Journal of STEM 5, no. 3 (2024): 55–63. https://doi.org/10.5281/zenodo.14028816.

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There have been many research works to digitalize Urdu Characters through machine learning algorithms. The algorithms that were already used for Urdu Optical Character Recognition [OCR] are Convolutional Neural Network [CNN], Recurrent Neural Network [RNN], and Transformer etc. There are also many machine learning algorithms that have not been used for Urdu OCR e.g Support Vector Machine, Graph Neural Network etc. This research paper proposes a comparative study between the performances of the already implemented Urdu OCR on some of following algorithms like Convolutional Neural Network/ Trans

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6

Yasin, Mehvish, and Naveen Kumar Gondhi. "A Comparative Analysis on Nastaliq Style Urdu Character Recognition." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 284–89. http://dx.doi.org/10.1166/jctn.2020.8663.

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Optical Character Recognition (OCR) has emerged as an interesting research field. Lot of work has been declared in Urdu script based on various approaches and diverse methodologies have been put forward on Nastaliq font style to get the desired output. The paper presents a survey on different techniques of OCR and ends up with the comparative analysis of Urdu character recognition based on accuracy and other performance parameters. This exploration directly implies that the nonpresence of Urdu OCR has restricted the idea on advanced Urdu library and thus, drives a pathway for enormous research

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7

Shafi, Muhammad, and Kashif Zia. "Urdu character recognition: a systematic literature review." International Journal of Applied Pattern Recognition 6, no. 4 (2021): 283. http://dx.doi.org/10.1504/ijapr.2021.10042503.

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8

Shafi, Muhammad, and Kashif Zia. "Urdu character recognition: a systematic literature review." International Journal of Applied Pattern Recognition 6, no. 4 (2021): 283. http://dx.doi.org/10.1504/ijapr.2021.118914.

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9

Khan, Khalil, Rehan Ullah, Nasir Ahmad Khan, and Khwaja Naveed. "Urdu Character Recognition using Principal Component Analysis." International Journal of Computer Applications 60, no. 11 (2012): 1–4. http://dx.doi.org/10.5120/9733-2082.

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10

Shaina*, Harpreet Kaur Bajaj. "ISOLATED CHARACTER RECOGNITION USING HIERARCHICAL APPROACH WITH SVM CLASSIFIER." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 9 (2016): 570–75. https://doi.org/10.5281/zenodo.154564.

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This paper proposes a method for Urdu language text. Character recognition is obtained by OCR. This paper represents the effectiveness of characters with SVM Classifier using Hierarchical approach. SVM is a useful technique for data classification. The objective of SVM is to generate a model which predicts the target value. The work is done on Sindhi Character Set. The experiment shows that character recognition with SVM Classifier achieves a recognition rate of 93.0481%.

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Buchteile zum Thema "Urdu Character Recognition"

1

Zargar, Hisham, Ruba Almahasneh, and László T. Kóczy. "Automatic Recognition of Handwritten Urdu Characters." In Studies in Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74970-5_19.

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Konferenzberichte zum Thema "Urdu Character Recognition"

1

Sardar, Shuwair, and Abdul Wahab. "Optical character recognition system for Urdu." In 2010 International Conference on Information and Emerging Technologies (ICIET). IEEE, 2010. http://dx.doi.org/10.1109/iciet.2010.5625694.

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2

Safdar, Quara-Tul-Ain, and Kamran Ullah Khan. "Online Urdu Handwritten Character Recognition: Initial Half Form Single Stroke Characters." In 2014 12th International Conference on Frontiers of Information Technology (FIT). IEEE, 2014. http://dx.doi.org/10.1109/fit.2014.61.

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3

Lodhi, Saeed M., and M. A. Matin. "Urdu character recognition using next-generation optical networks." In Frontiers in Optics. OSA, 2003. http://dx.doi.org/10.1364/fio.2003.mt55.

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4

Ahmad, Zaheer, Jehanzeb Khan Orakzai, and Inam Shamsher. "Urdu compound Character Recognition using feed forward neural networks." In 2009 2nd IEEE International Conference on Computer Science and Information Technology. IEEE, 2009. http://dx.doi.org/10.1109/iccsit.2009.5234683.

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5

Hussain, Syed Afaq, Safdar Zaman, and Muhammad Ayub. "A Self Organizing Map based Urdu Nasakh character recognition." In 2009 International Conference on Emerging Technologies (ICET). IEEE, 2009. http://dx.doi.org/10.1109/icet.2009.5353161.

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6

Shah, Z. A. "Ligature based optical character recognition of Urdu- Nastaleeq font." In International Multi Topic Conference, 2002. Abstracts. INMIC 2002. IEEE, 2002. http://dx.doi.org/10.1109/inmic.2002.1310132.

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7

Lodhi, S. M., and M. A. Matin. "Urdu character recognition using fourier descriptors for optical networks." In Optics & Photonics 2005, edited by Khan M. Iftekharuddin and Abdul A. S. Awwal. SPIE, 2005. http://dx.doi.org/10.1117/12.612650.

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8

Ali, Asghar, Mark Pickering, and Kamran Shafi. "Urdu Natural Scene Character Recognition using Convolutional Neural Networks." In 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR). IEEE, 2018. http://dx.doi.org/10.1109/asar.2018.8480202.

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9

Megherbi, Dalila B., Saeed M. Lodhi, and Azzoz J. Boulenouar. "Fuzzy-logic-model-based technique with application to Urdu character recognition." In Electronic Imaging, edited by Nasser M. Nasrabadi and Aggelos K. Katsaggelos. SPIE, 2000. http://dx.doi.org/10.1117/12.382911.

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10

Chandio, Asghar Ali, Mark Pickering, and Kamran Shafi. "Character classification and recognition for Urdu texts in natural scene images." In 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). IEEE, 2018. http://dx.doi.org/10.1109/icomet.2018.8346341.

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