Pose recognition using cross correlation for static images of Urdu sign language

Sami, Muhammad; Ahmed, Habib; Wahid, Ali; Siraj, Usama; Ahmed, Faizan; Shahid, Shahab; Shah, Syed Irtiza Ali

doi:10.1109/icreate.2014.6828365

Cited by 5 publications

(5 citation statements)

References 16 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%

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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%

“…The author’s first concern is the lack of publicly available datasets containing images of individuals making the signs. Only one such dataset is available in Mendeley by Imran et al in Sami (2014) in 2021, which is very recent. There are so many things that can be pointed out as a flaw.…”

Section: Discussionmentioning

confidence: 99%

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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“…Table 7 showcases the studies along with their respective years, datasets used, and remarks for each study. Conference ACM [27] Conference ACM [28][29][30][31][32][33] Conference IEEE [34] Journal Pensee Journal [35][36][37][38][39][40][41] Conference IEEE [42] Conference Elsevier [43] Conference ACM [44][45][46] Conference ACM [47] Conference British Machine Vision Conference [48][49][50] Conference IEEE [51] Numerous publicly available datasets are used by different articles. Some of them can be accessed via links shown in Table 7(b).…”

Section: Rq3: What Are the Types Of Datasets Used For Signmentioning

confidence: 99%

Exploring Sign Language Detection on Smartphones: A Systematic Review of Machine and Deep Learning Approaches

Alam,

Hameed,

Ziar

2024

Advances in Human-Computer Interaction

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In this modern era of technology, most of the accessibility issues are handled with the help of smart devices and cutting-edge gadgets. Smartphones play a crucial role in addressing various accessibility challenges, including voice recognition, sign language detection and interpretation, navigation systems, speech-to-text conversion, and vice versa, among others. They are computationally powerful enough to handle and run numerous machine and deep learning applications. Among various accessibility challenges, speech disorders represent a disability where individuals struggle to communicate verbally. Similarly, hearing loss is a disability that impairs an individual’s ability to hear, necessitating reliance on gestures for communication. A significant challenge encountered by people with speech disorders, hearing loss, or both is their inability to effectively convey or receive messages from others. Hence, these individuals heavily depend on the sign language (a gesture-based communication) method, typically involving hand movements and expressions. To the best of our knowledge, there are currently no comprehensive review and/or survey articles available that cover the literature on speech disabilities and sign language detection and interpretation via smartphones utilizing machine learning and/or deep learning approaches. This study fills the gap in the literature by analyzing research publications on speech disabilities, published from 2012 to July 2023. A rigorous search and standard strategy for formulating the literature along with a well-defined theoretical framework for results and findings have been used. The paper has implications for practitioners and researchers working in accessibilities in general and smart/intelligent gadgets and applications for speech-disabled people in specific.

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“…Another recent work related to recognition of Urdu alphabets of Pakistan sign language is done by Sami et al [2]. There work is another addition regarding recognition of Pakistan sign language using computer vision techniques.…”

Section: Computing Aid For Deafmentioning

confidence: 99%

Pakistan Sign Language: Computer Vision Analysis & Recommendations

Hassan¹,

Farooq

Abid

et al. 2015

VFAST trans. softw. eng.

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pk 1. Introduction. It has been discussed above that a noticeable amount of deaf community exists in Pakistan. A detail study has been presented based upon statistical data regarding the deaf people in Pakistan [1][2]. About 3.3 million of Pakistanis are suffering from any kinds of disability in which 0.24 million are impaired of hearing which approximates to 7.4% of overall disables. A very important point is that 55% of the total disabled lie in the age group from 5-29 years. Another statistics provide province wise breakdown and facilities for the deaf provided by Government of Pakistan. The report is jointly prepared from statistics which are taken from the Census Report of Pakistan 1998, State of World Refugees and UNHCR Global Report 2002 available at [3]. Along with that various organizations from Pakistan and across from the world made studies about the amount of hearing impaired in Pakistan and the facilities available to them in [4-7]. This huge quantity of hearing impaired people demands special attention from the community for minimizing communication gap between them and normal individuals of society. The goal of minimizing communication gap among the deaf and between a deaf and normal man can only be reduced using some sort of signs or gestures. There exist various organizations and research groups in Pakistan which are actively working for development of sign or gestures based languages for Pakistani deaf [8, 9]. As per their opinion, these sign/gesture based languages will help them in several ways like performing day to day activities in a casual manner, helping them in education and reducing the gap between a deaf and normal individual. The unavailability of any standard sign language and special deaf teachers are also the major problems in deaf communication and education in Pakistan as discussed by Bushra et al [10]. Apart from that people have also done lots of efforts to aid the deaf community of Pakistan in various ways. The details of various efforts made are available at [11]. They have observed their behaviors, cultures. Their interests, hobbies and extracurricular activities were also been observed. Their response towards marital life

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Pose recognition using cross correlation for static images of Urdu sign language

Cited by 5 publications

References 16 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

Exploring Sign Language Detection on Smartphones: A Systematic Review of Machine and Deep Learning Approaches

Pakistan Sign Language: Computer Vision Analysis & Recommendations

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