Urdu handwritten text recognition: a survey

Husnain, Mujtaba; Missen, Malik Muhammad Saad; Mumtaz, Shahzad; Coustaty, Mickaël; Luqman, Muhammad; Ogier, Jean-Marc

doi:10.1049/iet-ipr.2019.0401

Cited by 15 publications

(14 citation statements)

References 52 publications

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“…The suggested DConvAEN-Net model scored 95.21% on BanglaLekha-Isolated for 84 classes, 92.40% on CMATERdb 3.1 for 238 classes, and 95.53% on Ekush for 122 classes using this supervised and unsupervised learning technique [34]. Husnain et al [35] performed a survey on off-line Urdu hand written text recognition. According to the [35], Urdu is derived from Arabic language and it is bidirectional and cursive in its nature so it (Urdu) has almost same challenges as Arabic have for recognition purpose but Urdu has higher complexity.…”

Section: Related Workmentioning

confidence: 99%

“…Husnain et al [35] performed a survey on off-line Urdu hand written text recognition. According to the [35], Urdu is derived from Arabic language and it is bidirectional and cursive in its nature so it (Urdu) has almost same challenges as Arabic have for recognition purpose but Urdu has higher complexity. Authors presented survey of articles published from 2004-2019 with 04 levels which are: character, word, ligature, and sentence level [35].…”

Section: Related Workmentioning

confidence: 99%

“…According to the [35], Urdu is derived from Arabic language and it is bidirectional and cursive in its nature so it (Urdu) has almost same challenges as Arabic have for recognition purpose but Urdu has higher complexity. Authors presented survey of articles published from 2004-2019 with 04 levels which are: character, word, ligature, and sentence level [35]. All public available handwritten datasets are highlighted and some comparison has been performed with commonly used datasets and methods.…”

Section: Related Workmentioning

confidence: 99%

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Handwritten Urdu Characters and Digits Recognition Using Transfer Learning and Augmentation With AlexNet

et al. 2022

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Automated recognition of handwritten characters and digits is a challenging task. Although a significant amount of literature exists for automatic recognition of handwritten characters of English and other major languages in the world, there exists a wide research gap due to lack of research for recognition of Urdu language. The variations in writing style, shape and size of individual characters and similarities with other characters add to the complexity for accurate classification of handwritten characters. Deep neural networks have emerged as a powerful technology for automated classification of character patters and object images. Although deep networks are known to provide remarkable results on large-scale datasets with millions of images, however the use of deep networks for small image datasets is still challenging. The purpose of this research is to present a classification framework for automatic recognition of handwritten Urdu character and digits with higher recognition accuracy by utilizing theory of transfer learning and pretrained Convolution Neural Networks (CNN). The performance of transfer learning is evaluated in different ways: by using pre-trained AlexNet CNN model with Support Vector Machine (SVM) classifier, and finetuned AlexNet for extracting features and classification. We have fine-tuned AlexNet hyper-parameters to achieve higher accuracy and data augmentation is performed to avoid over-fitting. Experimental results and the quantitative comparisons demonstrate the effectiveness of the proposed research for recognition of handwritten characters and digits using fine-tuned AlexNet. The proposed research based on fine-tuned AlexNet outperforms the related state-of-the-art research thereby achieving a classification accuracy of 97.08%, 98.21%, 94.92% for urdu characters, digits and hybrid datasets respectively. The presented methods can be applied for research on Urdu characters and in diverse domains such as handwritten text image retrieval, reading postal addresses, bank's cheque processing, preserving and digitization of manuscripts from old ages.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Handwritten Urdu Characters and Digits Recognition Using Transfer Learning and Augmentation With AlexNet

et al. 2022

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“…The authors highlighted the issues of multilingual text and real-time performance of current systems [23]. Other recent surveys have analysed specific language-based techniques such as Urdu [24], but also omit information related to their implementation or their multiple configurations.…”

Section: Latest Surveys On Text Spottingmentioning

confidence: 99%

A survey on methods, datasets and implementations for scene text spotting

Blanco‐Medina

Fidalgo

Alegre

et al. 2022

IET Image Processing

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Text Spotting is the union of the tasks of detection and transcription of the text that is present in images. Due to the various problems often found when retrieving text, such as orientation, aspect ratio, vertical text or multiple languages in the same image, this can be a challenging task. In this paper, the most recent methods and publications in this field are analysed and compared. Apart from presenting features already seen in other surveys, such as their architectures and performance on different datasets, novel perspectives for comparison are also included, such as the hardware, software, backbone architectures, main problems to solve, or programming languages of the algorithms. The review highlights information often omitted in other studies, providing a better understanding of the current state of research in Text Spotting, from 2016 to 2022, current problems and future trends, as well as establishing a baseline for future methods development, comparison of results and serving as guideline for choosing the most appropriate method to solve a particular problem.

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“…As mentioned in the earlier section, there is a lack of an appropriate and concise data corpus containing Urdu handwritten characters and numerals to perform text recognition tasks at the character level. ere are some publicly available datasets of Urdu handwritten text like Urdu Nastaliq Handwritten Dataset (UNHD) (Ahmed et al [22]; Das et al [23]; Husnain et al [24]; Sagheer et al [25]), but unfortunately, the data sets have only the Urdu handwritten numerals. Furthermore, these instances are not enough to apply state-of-the-art machine learning algorithms to get better results.…”

Section: Dataset Preparationmentioning

confidence: 99%

Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network

et al. 2021

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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 available dataset of such type till date, then (ii) applied classical approaches of dimensionality reduction and data visualization like Principal Component Analysis (PCA), Autoencoders (AE) in comparison with t-Stochastic Neighborhood Embedding (t-SNE), and (iii) used the reduced dimensions obtained through PCA, AE, and t-SNE for recognition of Urdu handwritten characters and numerals using a deep network like Convolution Neural Network (CNN). The accuracy achieved in recognition of Urdu characters and numerals among the approaches for the same task is found to be much better. The novelty lies in the fact that the resulting reduced dimensions are used for the first time for the recognition of Urdu handwritten text at the character level instead of using the whole multidimensional data. This results in consuming less computation time with the same accuracy when compared with processing time consumed by recognition approaches applied to other datasets for the same task using the whole data.

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Urdu handwritten text recognition: a survey

Cited by 15 publications

References 52 publications

Handwritten Urdu Characters and Digits Recognition Using Transfer Learning and Augmentation With AlexNet

Handwritten Urdu Characters and Digits Recognition Using Transfer Learning and Augmentation With AlexNet

A survey on methods, datasets and implementations for scene text spotting

Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network

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