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Journal articles on the topic 'Urdu Handwritten Characters'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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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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11

Asghar, Ali Chandio, Leghari Mehwish, Orangzeb Panhwar Ali, Zaman Nizamani Shah, and Leghari Mehjabeen. "Deep learning-based isolated handwritten Sindhi character recognition." Indian Journal of Science and Technology 13, no. 25 (2020): 2565–74. https://doi.org/10.17485/IJST/v13i25.914.

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Abstract <strong>Motivation :</strong> The problem of handwritten text recognition is vastly studied since last few decades. Many innovative ideas have been developed, where state-of-the-art accuracy is achieved for the English, Chinese or Indian scripts.The recent developments for the cursive scripts such as Arabic and Urdu handwritten text recognition have achieved remarkable accuracy. However, for the Sindhi script, existing systems have not shown significant results and the problem is still an open challenge. Several challenges such as variations in writing styles, joined text, ligatu

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12

Khan, Sulaiman, and Shah Nazir. "Deep Learning Based Pashto Characters Recognition." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 58, no. 3 (2022): 49–58. http://dx.doi.org/10.53560/ppasa(58-3)743.

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In artificial intelligence, text identification and analysis that are based on images play a vital role in the text retrieving process. Automatic text recognition system development is a difficult task in machine learning, but in the case of cursive languages, it poses a big challenge to the research community due to slight changes in character’s shapes and the unavailability of a standard dataset. While this recognition task becomes more challenging in the case of Pashto language due to a large number of characters in its dataset than other similar cursive languages (Persian, Urdu, Arabic) an

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13

Jan, Z., M. Shabir, M. A. Khan, A. Ali, and M. Muzammal. "Online Urdu Handwriting Recognition System Using Geometric Invariant Features." Nucleus 53, no. 2 (2016): 89–98. https://doi.org/10.71330/thenucleus.2016.216.

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Online touch sensitive devices facilitate users by providing an easy way for inputting online handwritten text. Many useful applications are developed for other cursive script language and are practically used in different fields like banking, commerce, academics, administration and education etc. There are also some systems proposed for online Urdu handwriting recognition but either they have low accuracy rates or high constraints on user while writing. Online Urdu handwriting recognition is a difficult task due to its cursive property and writing complexity. The proposed system tries to reco

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14

Hamza, Ameer, Shengbing Ren, and Usman Saeed. "ET-Network: A novel efficient transformer deep learning model for automated Urdu handwritten text recognition." PLOS ONE 19, no. 5 (2024): e0302590. http://dx.doi.org/10.1371/journal.pone.0302590.

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Automatic Urdu handwritten text recognition is a challenging task in the OCR industry. Unlike printed text, Urdu handwriting lacks a uniform font and structure. This lack of uniformity causes data inconsistencies and recognition issues. Different writing styles, cursive scripts, and limited data make Urdu text recognition a complicated task. Major languages, such as English, have experienced advances in automated recognition, whereas low-resource languages, such as Urdu, still lag. Transformer-based models are promising for automated recognition in high- and low-resource languages such as Urdu

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15

Khan, H. R., M. A. Hasan, M. Kazmi, N. Fayyaz, H. Khalid, and S. A. Qazi. "A Holistic Approach to Urdu Language Word Recognition using Deep Neural Networks." Engineering, Technology & Applied Science Research 11, no. 3 (2021): 7140–45. http://dx.doi.org/10.48084/etasr.4143.

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Urdu is one of the most popular languages in the world. It is a Persianized standard register of the Hindi language with considerable and valuable literature. While digital libraries are constantly replacing conventional libraries, a vast amount of Urdu literature is still handwritten. Digitizing this handwritten literature is essential to preserve it and make it more accessible. Nevertheless, the scarcity of Urdu Optical Character Recognition (OCR) research limits a digital library's scope to a manual document search. The limited research work in this area is mainly due to the complexity of U

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16

Ahmed, Saad Bin, Saeeda Naz, Salahuddin Swati, and Muhammad Imran Razzak. "Handwritten Urdu character recognition using one-dimensional BLSTM classifier." Neural Computing and Applications 31, no. 4 (2017): 1143–51. http://dx.doi.org/10.1007/s00521-017-3146-x.

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17

Rizvi, S. S. R., A. Sagheer, K. Adnan, and A. Muhammad. "Optical Character Recognition System for Nastalique Urdu-Like Script Languages Using Supervised Learning." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 10 (2019): 1953004. http://dx.doi.org/10.1142/s0218001419530045.

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There are two main techniques to convert written or printed text into digital format. The first technique is to create an image of written/printed text, but images are large in size so they require huge memory space to store, as well as text in image form cannot be undergo further processes like edit, search, copy, etc. The second technique is to use an Optical Character Recognition (OCR) system. OCR’s can read documents and convert manual text documents into digital text and this digital text can be processed to extract knowledge. A huge amount of Urdu language’s data is available in handwrit

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18

Bhatti, Aamna, Ameera Arif, Waqar Khalid, et al. "Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques." Applied Sciences 13, no. 3 (2023): 1624. http://dx.doi.org/10.3390/app13031624.

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Urdu is a complex language as it is an amalgam of many South Asian and East Asian languages; hence, its character recognition is a huge and difficult task. It is a bidirectional language with its numerals written from left to right while script is written in opposite direction which induces complexities in the recognition process. This paper presents the recognition and classification of a novel Urdu numeral dataset using convolutional neural network (CNN) and its variants. We propose custom CNN model to extract features which are used by Softmax activation function and support vector machine

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19

Ijaz, Irtaza, Abdallah Namoun, Nasser Aljohani, et al. "Automated compilation of Urdu poetry handwritten image datasets for optical character recognition." MethodsX 14 (June 2025): 103130. https://doi.org/10.1016/j.mex.2024.103130.

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20

Chhajro, M. Ameen. "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. http://dx.doi.org/10.17485/ijst/v13i17.113.

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21

K.O, Mohammed Aarif, and Sivakumar Poruran. "OCR-Nets: Variants of Pre-trained CNN for Urdu Handwritten Character Recognition via Transfer Learning." Procedia Computer Science 171 (2020): 2294–301. http://dx.doi.org/10.1016/j.procs.2020.04.248.

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22

Singh, Pawan Kumar, Supratim Das, Ram Sarkar, and Mita Nasipuri. "Line Parameter based Word-Level Indic Script Identification System." International Journal of Computer Vision and Image Processing 6, no. 2 (2016): 18–41. http://dx.doi.org/10.4018/ijcvip.2016070102.

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In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts. Since Optical Character Recognition (OCR) engines are usually script-dependent, automatic text recognition in multi-script environment requires a pre-processing module that helps identifying the scripts before processing the same through the respective OCR engine. The work becomes more challenging when it deals with handwritten document which is still a less explored research area. In this paper, a line parameter based approach is presented to identify the handwritt

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23

Hamid, Irfan, Rameez Raja, Monika Anand, Vijay Karnatak, and Aleem Ali. "Comprehensive robustness evaluation of an automatic writer identification system using convolutional neural networks." Journal of Autonomous Intelligence 7, no. 1 (2023). http://dx.doi.org/10.32629/jai.v7i1.763.

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<p>This research paper presents a convolutional neural network (CNN) model for identifying handwritten Urdu characters. A dataset of 38 fundamental Urdu characters from 100 different writers in the Kashmir valley was manually collected. The developed system was trained on a training dataset of 30,400 samples and verified on a test dataset of 7600 samples, and it outperformed previously proposed AI based writer identification systems in Urdu language with an identification rate of 91.44 percent for 38 classes. This study highlights the effectiveness of deep learning techniques in solving

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24

Misgar, Muzafar Mehraj, Faisel Mushtaq, Surinder Singh Khurana, and Munish Kumar. "Recognition of offline handwritten Urdu characters using RNN and LSTM models." Multimedia Tools and Applications, June 17, 2022. http://dx.doi.org/10.1007/s11042-022-13320-1.

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25

Nabi, Syed Tufael, Munish Kumar, and Paramjeet Singh. "A convolution deep architecture for gender classification of urdu handwritten characters." Multimedia Tools and Applications, January 31, 2024. http://dx.doi.org/10.1007/s11042-024-18415-5.

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26

Rasheed, Aqsa, Nouman Ali, Bushra Zafar, Amsa Shabbir, Muhammad Sajid, and Muhammad Tariq Mahmood. "Handwritten Urdu Characters and Digits Recognition Using Transfer Learning and Augmentation with AlexNet." IEEE Access, 2022, 1. http://dx.doi.org/10.1109/access.2022.3208959.

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27

Nabi, Syed Tufael, Munish Kumar, and Paramjeet Singh. "Correction to: A convolution deep architecture for gender classification of Urdu handwritten characters." Multimedia Tools and Applications, August 14, 2024. http://dx.doi.org/10.1007/s11042-024-20020-5.

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28

Ali, Hazrat, Ahsan Ullah, Talha Iqbal, and Shahid Khattak. "Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network." SN Applied Sciences 2, no. 2 (2020). http://dx.doi.org/10.1007/s42452-019-1914-1.

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29

Rizvi, Syed Saqib Raza, Muhammad Adnan Khan, Sagheer Abbas, Muhammad Asadullah, Nida Anwer, and Areej Fatima. "Deep Extreme Learning Machine-Based Optical Character Recognition System for Nastalique Urdu-Like Script Languages." Computer Journal, June 20, 2020. http://dx.doi.org/10.1093/comjnl/bxaa042.

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Abstract Optical character recognition systems convert printed or handwritten scripts into digital text formats like ASCII or UNICODE. Urdu-like script languages like Urdu, Punjabi and Sindhi are widely spoken languages of the world, especially in Asia. An enormous amount of printed and handwritten text of such languages exist, which needs to be converted into computer-understandable formats for knowledge extraction. In this study, extreme learning machine’s (ELM’s) most recently proposed variant called deep extreme learning machine (DELM)-based optical character recognition (OCR) system is pr

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30

Mushtaq, Faisel, Muzafar Mehraj Misgar, Munish Kumar, and Surinder Singh Khurana. "UrduDeepNet: offline handwritten Urdu character recognition using deep neural network." Neural Computing and Applications, June 7, 2021. http://dx.doi.org/10.1007/s00521-021-06144-x.

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31

Alam, Md Afaque, and Dr Muqeem Ahmed. "Leveraging Deep CNNs for Efficient Urdu Handwritten Character and Digit Recognition." SSRN Electronic Journal, 2025. https://doi.org/10.2139/ssrn.5191580.

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32

Sahay, Rajat, and Mickael Coustaty. "An Enhanced Prototypical Network Architecture for Few-Shot Handwritten Urdu Character Recognition." IEEE Access, 2023, 1. http://dx.doi.org/10.1109/access.2023.3263721.

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33

Ain Safdar, Quara tul, Kamran Ullah Khan, and Liangrui Peng. "A Novel Similar Character Discrimination Method for Online Handwritten Urdu Character Recognition in Half Forms." Scientia Iranica, August 11, 2018, 0. http://dx.doi.org/10.24200/sci.2018.20826.

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34

Sultana, Tajwar, Abdul Rehman, Bilal Ahmed, et al. "Towards Development of Real-Time Handwritten Urdu Character to Speech Conversion System for Visually Impaired." International Journal of Advanced Computer Science and Applications 7, no. 12 (2016). http://dx.doi.org/10.14569/ijacsa.2016.071204.

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35

"A Novel Deep Convolutional Neural Network Architecture Based on Transfer Learning for Handwritten Urdu Character Recognition." Tehnicki vjesnik - Technical Gazette 27, no. 4 (2020). http://dx.doi.org/10.17559/tv-20190319095323.

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36

Khan, Sulaiman, Shah Nazir, and Habib Ullah Khan. "Analysis of Cursive Text Recognition Systems: A Systematic Literature Review." ACM Transactions on Asian and Low-Resource Language Information Processing, April 13, 2023. http://dx.doi.org/10.1145/3592600.

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Regional and cultural diversities around the world have given birth to a large number of writing systems and scripts, which consist of varying character sets. Developing an optimal OCR for such a varying and large character set is a challenging task. Unlimited variations in handwritten text due to mood swings, varying writing styles, changes in medium of writing, and many more puzzle the research community. To overcome this problem, researchers have proposed various techniques for the automatic recognition of cursive languages like Urdu, Pashto, and Arabic. With the passage of time, the field

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