A computer vision-based system for recognition and classification of Urdu sign language dataset

Zahid, Hina; Rashid, Munaf; Syed, Sidra Abid; Ullah, Rafi; Asif, Muhammad; Khan, Muzammil Ahmad; Mujeeb, Amenah Abdul; Khan, Ali Haider

doi:10.7717/peerj-cs.1174

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…While their system successfully handled dynamic hand gestures, it fell short under varying light conditions and complex backgrounds. Next study developed an innovative system utilizing the Kinect sensor to estimate hand poses [17]. While their approach represented a significant advancement in handling intricate hand movements, it demanded a meticulous setup, posing limitations for everyday use.…”

Section: B Depth Sensor-based Methodsmentioning

confidence: 99%

Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

Kozhamkulova,

Nurlybaeva,

Kuntunova

et al. 2023

IJACSA

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This paper presents a novel approach to fingerspelling recognition in real-time, utilizing a twodimensional Convolutional Neural Network (2D CNN). Existing recognition systems often fall short in real-world conditions due to variations in illumination, background, and user-specific characteristics. Our method addresses these challenges, delivering significantly improved performance. Leveraging a robust 2D CNN architecture, the system processes image sequences representing the dynamic nature of fingerspelling. We focus on low-level spatial features and temporal patterns, thereby ensuring a more accurate capture of the intricate nuances of fingerspelling. Additionally, the incorporation of real-time video feed enhances the system's responsiveness. We validate our model through comprehensive experiments, showcasing its superior recognition rate over current methods. In scenarios involving varied lighting, different backgrounds, and distinct user behaviors, our system consistently outperforms. The findings demonstrate that the 2D CNN approach holds promise in improving fingerspelling recognition, thereby aiding communication for the hearing-impaired community. This work paves the way for further exploration of deep learning applications in real-time sign language interpretation. This research bears profound implications for accessibility and inclusivity in communication technology.

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Section: B Depth Sensor-based Methodsmentioning

confidence: 99%

Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

Kozhamkulova,

Nurlybaeva,

Kuntunova

et al. 2023

IJACSA

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A neural-network based web application on real-time recognition of Pakistani sign language

Mujeeb,

Khan,

Khalid

et al. 2024

Engineering Applications of Artificial Intelligence

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Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete

Hassan,

Syed,

Ali

et al. 2024

PeerJ Computer Science

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Background Concrete, a fundamental construction material, stands as a significant consumer of virgin resources, including sand, gravel, crushed stone, and fresh water. It exerts an immense demand, accounting for approximately 1.6 billion metric tons of Portland and modified Portland cement annually. Moreover, addressing extreme conditions with exceptionally nonlinear behavior necessitates a laborious calibration procedure in structural analysis and design methodologies. These methods are also difficult to execute in practice. To reduce time and effort, ML might be a viable option. Material and Methods A set of keywords are designed to perform the search PubMed search engine with filters to not search the studies below the year 2015. Furthermore, using PRISMA guidelines, studies were selected and after screening, a total of 42 studies were summarized. The PRISMA guidelines provide a structured framework to ensure transparency, accuracy, and completeness in reporting the methods and results of systematic reviews and meta-analyses. The ability to methodically and accurately connect disparate parts of the literature is often lacking in review research. Some of the trickiest parts of original research include knowledge mapping, co-citation, and co-occurrence. Using this data, we were able to determine which locations were most active in researching machine learning applications for concrete, where the most influential authors were in terms of both output and citations and which articles garnered the most citations overall. Conclusion ML has become a viable prediction method for a wide variety of structural industrial applications, and hence it may serve as a potential successor for routinely used empirical model in the design of concrete structures. The non-ML structural engineering community may use this overview of ML methods, fundamental principles, access codes, ML libraries, and gathered datasets to construct their own ML models for useful uses. Structural engineering practitioners and researchers may benefit from this article’s incorporation of concrete ML studies as well as structural engineering datasets. The construction industry stands to benefit from the use of machine learning in terms of cost savings, time savings, and labor intensity. The statistical and graphical representation of contributing authors and participants in this work might facilitate future collaborations and the sharing of novel ideas and approaches among researchers and industry professionals. The limitation of this systematic review is that it is only PubMed based which means it includes studies included in the PubMed database.

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A computer vision-based system for recognition and classification of Urdu sign language dataset

Cited by 3 publications

References 36 publications

Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

A neural-network based web application on real-time recognition of Pakistani sign language

Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete

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