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Thematische Bibliographien / Urdu Handwritten Characters

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Auswahl der wissenschaftlichen Literatur zum Thema „Urdu Handwritten Characters“

Autor: Grafiati

Veröffentlicht am 7. Juni 2025

Zuletzt aktualisiert am 2. August 2025

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Zeitschriftenartikel zum Thema "Urdu Handwritten Characters"

1

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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2

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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3

Husnain, Mujtaba, Malik Muhammad Saad Missen, Shahzad Mumtaz, et al. "Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network." Complexity 2021 (September 2, 2021): 1–15. http://dx.doi.org/10.1155/2021/4383037.

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In this paper, we make use of the 2-dimensional data obtained through t-Stochastic Neighborhood Embedding (t-SNE) when applied on high-dimensional data of Urdu handwritten characters and numerals. The instances of the dataset used for experimental work are classified in multiple classes depending on the shape similarity. We performed three tasks in a disciplined order; namely, (i) we generated a state-of-the-art dataset of both the Urdu handwritten characters and numerals by inviting a number of native Urdu participants from different social and academic groups, since there is no publicly avai

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4

Husnain, Mujtaba, Malik Muhammad Saad Missen, Shahzad Mumtaz, et al. "Recognition of Urdu Handwritten Characters Using Convolutional Neural Network." Applied Sciences 9, no. 13 (2019): 2758. http://dx.doi.org/10.3390/app9132758.

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In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best wh

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5

G. Mahdi, Mohamed, Ahmed Sleem, and Ibrahim Elhenawy. "Deep Learning Algorithms for Arabic Optical Character Recognition: A Survey." Multicriteria Algorithms with Applications 2 (January 26, 2024): 65–79. http://dx.doi.org/10.61356/j.mawa.2024.26861.

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In recent years, deep learning has begun to supplant traditional machine learning algorithms in a variety of fields, including machine translation (MT), pattern recognition (PR), natural language processing (NLP), speech recognition (SR), and computer vision. Systems for optical character recognition (OCR) have recently been developed using deep learning techniques with great success. Within the area of pattern recognition and computer vision, the procedure of handwritten character recognition is still considered to be one of the most challenging. The height, orientation, and width of the hand

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6

Jiang, Weiwei. "Evaluation of deep learning models for Urdu handwritten characters recognition." Journal of Physics: Conference Series 1544 (May 2020): 012016. http://dx.doi.org/10.1088/1742-6596/1544/1/012016.

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7

Rehman, Muhammad Zubair, Nazri Mohd. Nawi, Mohammad Arshad, and Abdullah Khan. "Recognition of Cursive Pashto Optical Digits and Characters with Trio Deep Learning Neural Network Models." Electronics 10, no. 20 (2021): 2508. http://dx.doi.org/10.3390/electronics10202508.

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Pashto is one of the most ancient and historical languages in the world and is spoken in Pakistan and Afghanistan. Various languages like Urdu, English, Chinese, and Japanese have OCR applications, but very little work has been conducted on the Pashto language in this perspective. It becomes more difficult for OCR applications to recognize handwritten characters and digits, because handwriting is influenced by the writer’s hand dynamics. Moreover, there was no publicly available dataset for handwritten Pashto digits before this study. Due to this, there was no work performed on the recognition

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8

Jameel, Mohd. "A REVIEW ON RECOGNITION OF HANDWRITTEN URDU CHARACTERS USING NEURAL NETWORKS." International Journal of Advanced Research in Computer Science 8, no. 9 (2017): 727–30. http://dx.doi.org/10.26483/ijarcs.v8i9.4759.

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9

Uddin, Imran, Dzati A. Ramli, Abdullah Khan, et al. "Benchmark Pashto Handwritten Character Dataset and Pashto Object Character Recognition (OCR) Using Deep Neural Network with Rule Activation Function." Complexity 2021 (March 4, 2021): 1–16. http://dx.doi.org/10.1155/2021/6669672.

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In the area of machine learning, different techniques are used to train machines and perform different tasks like computer vision, data analysis, natural language processing, and speech recognition. Computer vision is one of the main branches where machine learning and deep learning techniques are being applied. Optical character recognition (OCR) is the ability of a machine to recognize the character of a language. Pashto is one of the most ancient and historical languages of the world, spoken in Afghanistan and Pakistan. OCR application has been developed for various cursive languages like U

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10

Jameel, Mohd, and Sanjay Kumar. "Offline Recognition of Handwritten Urdu Characters using B Spline Curves: A Survey." International Journal of Computer Applications 157, no. 1 (2017): 28–34. http://dx.doi.org/10.5120/ijca2017912604.

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Buchteile zum Thema "Urdu Handwritten Characters"

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 Handwritten Characters"

1

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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2

Khan, Kamran Ullah, and Ihtesham Haider. "Online recognition of multi-stroke handwritten Urdu characters." In 2010 International Conference on Image Analysis and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/iasp.2010.5476113.

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3

Haider, Ihtesham, and Kamran Ullah Khan. "Online recognition of single stroke handwritten Urdu characters." In 2009 IEEE 13th International Multitopic Conference (INMIC). IEEE, 2009. http://dx.doi.org/10.1109/inmic.2009.5383108.

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