Cursive-Text: A Comprehensive Dataset for End-to-End Urdu Text Recognition in Natural Scene Images

Chandio, Asghar Ali; Asikuzzaman, Md.; Pickering, Mark; Leghari, Mehwish

doi:10.1016/j.dib.2020.105749

Cited by 17 publications

(9 citation statements)

References 3 publications

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“…Figure 5 representing that which dataset is the most used and which is the least used and as per the resulting signal based EMG data has only used twice in Khan et al (2020b) and Khan et al (2020a) whereas dataset which contains the images of sign used four times in Halim & Abbas (2014) , Kanwal et al (2014) , Nasir et al (2014) and Imran et al (2021a) . The most used type of dataset are the character-based datasets in Chandio et al (2020) , Naseem et al (2019) , Sagheer et al (2010) , Ahmad et al (2017) , Sami, (2014) , Husnain et al (2019) , Gul et al (2020) Arafat & Iqbal (2020) and Ahmed et al (2017) . Also, in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Also, in Fig. 6 , we can observe that only five datasets are publically available out of four datasets ( Chandio et al, 2020 ; Sagheer et al, 2010 ; Arafat & Iqbal, 2020 ; Ahmed et al, 2017 ) contain either character, numeral, or sentence-based images. Only one dataset ( Liberati et al, 2009 ) is publically available, which is based on images of the visually impaired individual making signs of Urdu Language and the highest accuracy reported in publically available datasets is 97% by Chandio et al (2020) , which is reported on the text-based dataset and not on sign based dataset which is the actual lackness in this area.…”

Section: Resultsmentioning

confidence: 99%

“…The study is either published in a journal or conference. From Table 3 , we can analyzed that the SVM ( Chandio et al, 2020 ; Imran et al, 2021a ; Sagheer et al, 2010 ; Ahmad et al, 2017 ; Khan et al, 2020b ; Ahmed et al, 2017 ; Imran et al, 2021b ) and Neural Network ( Chandio et al, 2020 ; Naseem et al, 2019 ; Ahmad et al, 2007 ; Arafat & Iqbal, 2020 ; Sagheer et al, 2009 ; Naz et al, 2015 ; Ul-Hasan et al, 2013 ) is the commonly used classifier by researchers for the detection of Urdu Sign Language other than these both rest of the classifiers used only once i.e., DTW ( Halim & Abbas, 2014 ), HMM ( Gul et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

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Recognition of Urdu sign language: a systematic review of the machine learning classification

Zahid

Rashid

Hussain³

et al. 2022

PeerJ Computer Science

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Background and Objective Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning techniques have recently shown exceptional success and made significant progress. The primary objective of this research is to conduct a literature review on all the work completed on the recognition of Urdu Sign Language through machine learning classifiers to date. Materials and methods All the studies have been extracted from databases, i.e., PubMed, IEEE, Science Direct, and Google Scholar, using a structured set of keywords. Each study has gone through proper screening criteria, i.e., exclusion and inclusion criteria. PRISMA guidelines have been followed and implemented adequately throughout this literature review. Results This literature review comprised 20 research articles that fulfilled the eligibility requirements. Only those articles were chosen for additional full-text screening that follows eligibility requirements for peer-reviewed and research articles and studies issued in credible journals and conference proceedings until July 2021. After other screenings, only studies based on Urdu Sign language were included. The results of this screening are divided into two parts; (1) a summary of all the datasets available on Urdu Sign Language. (2) a summary of all the machine learning techniques for recognizing Urdu Sign Language. Conclusion Our research found that there is only one publicly-available USL sign-based dataset with pictures versus many character-, number-, or sentence-based publicly available datasets. It was also concluded that besides SVM and Neural Network, no unique classifier is used more than once. Additionally, no researcher opted for an unsupervised machine learning classifier for detection. To the best of our knowledge, this is the first literature review conducted on machine learning approaches applied to Urdu sign language.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Recognition of Urdu sign language: a systematic review of the machine learning classification

Zahid

Rashid

Hussain³

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

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“…However, the text in news ticker images generally appears at either bottom or top on images, which makes the text localization task easier. The largest Urdu scene text dataset is presented in [39], where author has collected 2500 natural outdoor images with three different languages text, Urdu, English and Sindhi. This dataset is further processed to get cropped isolated characters and word dataset.…”

Section: A Datasets Of Text In Natural Imagementioning

confidence: 99%

Towards AI-Enabled Approach for Urdu Text Recognition: A Legacy for Urdu Image Apprehension

et al. 2024

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Recognizing Urdu text in natural images is more challenging as compared to other languages, such as English, due to the cursive nature of Urdu script. However, Urdu scene text has not received enough attention from both industry and academia due to the lack of the dataset of Urdu text. We propose a largescale Urdu Scene Text Dataset (USTD) to address this problem, which is designed for Urdu scene text detection and recognition. The proposed dataset contains 29674 text annotations (17877 Urdu and 11797 English), 749725 characters in 6389 images. It covers a wide variety of text images with both Nastaleeq and Naskh writing styles, taken from different streets and roads of Pakistan. The vast diversity of this dataset makes it a benchmark to work on and train robust neural networks for the detection and recognition of cursive text. Besides, baseline results are also provided with several state-of-the-art networks, including TextBoxes++, Seglink, DB(ResNet-50) and EAST for text localization and Convolutional Recurrent Neural Network (CRNN) for text recognition. To further evaluate the performance of these models, we have used the most popular evaluation matrices of precision, recall, and F-measure. Our experimental outputs reveal that an end-to-end combination of DB(ResNet-50) and CRNN provides the best results with precision, recall, and F-measure of 0.7526, 0.5974, and 0.6660, respectively.

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“…Second, datasets for scene text localization and recognition are often limited to Latin script, which is considerably easier to process compared to cursive scripts (e.g. Japanese, Chinese, Persian and Arabic) [12], [13]. Moreover, these datasets are occasionally collected in a transportation context, which is again discouraging for road safety researchers.…”

Section: Introductionmentioning

confidence: 99%

ATTICA: A Dataset for Arabic Text-Based Traffic Panels Detection

et al. 2021

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Detection and recognition of traffic panels and their textual information are important applications of advanced driving assistance systems (ADAS). They can significantly contribute in enhancing road safety by optimizing the driving experience. However, these tasks might face two major challenges. First, the lack of suitable and good quality datasets. Second, the in-feasibility of global standardization of traffic panels in terms of shape, color and language of the written text. Present research is intensively directed toward Latin text-based panels, while other scripts such as Arabic remain quiet undervalued. In this paper, we address this issue by introducing ATTICA a , a new open-source multi-task dataset, consisting of two main sub-datasets: ATTICA_Sign for traffic signs/panels detection and ATTICA_Text for Arabic text extraction/recognition. Our dataset gathers 1215 images with 3173 traffic panel boxes, 870 traffic sign boxes and 7293 Arabic text boxes. In order to examine the utility and advantages of our dataset, we adopt stateof-the-art deep learning based approaches. The conducted experiments show promising results confirming the valuable addition of our dataset in this field of research.

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Cursive-Text: A Comprehensive Dataset for End-to-End Urdu Text Recognition in Natural Scene Images

Cited by 17 publications

References 3 publications

Recognition of Urdu sign language: a systematic review of the machine learning classification

Recognition of Urdu sign language: a systematic review of the machine learning classification

Towards AI-Enabled Approach for Urdu Text Recognition: A Legacy for Urdu Image Apprehension

ATTICA: A Dataset for Arabic Text-Based Traffic Panels Detection

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