Regional-CNN-based enhanced Turkish sign language recognition

Yirtici, Tolga; Yurtkan, Kamil

doi:10.1007/s11760-021-02082-2

Cited by 20 publications

(6 citation statements)

References 10 publications

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“…The authors ( Yirtici and Yurtkan, 2022 ) proposed a novel method for recognizing characters in Turkish Sign Language (TSL) using a transfer learning approach with a pre-trained Alexnet and a Region-based Convolutional Neural Network (R-CNN) object detector. The method achieved a commendable 99.7% average precision in recognizing TSL signs, demonstrating its efficacy compared to traditional methods.…”

Section: Review Of Related Workmentioning

confidence: 99%

A machine learning-driven web application for sign language learning

Orovwode,

Ibukun,

Abubakar

2024

Front. Artif. Intell.

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Addressing the increasing demand for accessible sign language learning tools, this paper introduces an innovative Machine Learning-Driven Web Application dedicated to Sign Language Learning. This web application represents a significant advancement in sign language education. Unlike traditional approaches, the application’s unique methodology involves assigning users different words to spell. Users are tasked with signing each letter of the word, earning a point upon correctly signing the entire word. The paper delves into the development, features, and the machine learning framework underlying the application. Developed using HTML, CSS, JavaScript, and Flask, the web application seamlessly accesses the user’s webcam for a live video feed, displaying the model’s predictions on-screen to facilitate interactive practice sessions. The primary aim is to provide a learning platform for those who are not familiar with sign language, offering them the opportunity to acquire this essential skill and fostering inclusivity in the digital age.

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Section: Review Of Related Workmentioning

confidence: 99%

A machine learning-driven web application for sign language learning

Orovwode,

Ibukun,

Abubakar

2024

Front. Artif. Intell.

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“…Additionally, sign language motions can be detected and recognized using computer vision; this is very helpful for people who have hearing loss. In order to convert sign language motions from video recordings into spoken or written language or text, sign language recognition systems use computer vision algorithms (Yirtici et al, 2022). Computer vision has the potential to be employed in a wide range of assistive technologies, from speech recognition and natural language processing systems to object detection and environmental control systems, in addition to these applications.…”

Section: Previous Work On Computer Vision In Assistive Technologies F...mentioning

confidence: 99%

Exploring the use of computer vision in assistive technologies for individuals with disabilities: A review

Sahoo,

Choudhury

2024

10.5267/j.jfs

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A wide range of obstacles faced by people with disabilities, such as visual impairment, motor disability, and communication difficulties, have shown significant promise for being addressed by computer vision. The state-of-the-art in computer vision-based assistive technology is examined in this report along with major future research topics and obstacles. In particular, this study explores how computer vision can be used for object recognition, navigation, facial recognition, sign language interpretation, and gesture-based control interfaces. It also discusses the benefits and drawbacks of various methodologies and technologies and offers examples of how computer vision can be incorporated into current assistive technologies to boost their efficacy. The ethical and privacy issues surrounding the use of computer vision in assistive technologies are covered in this study effort. The study also highlights the need for protocol standardization, better user-centered design, and the assessment of real-world effectiveness as future research objectives for improving the use of computer vision in assistive technology. Overall, this paper sheds light on how computer vision might completely alter the world of assistive technologies for people with impairments.

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“…Terdapat penelitian-penelitian sebelumnya yang memiliki ide dan dasar yang sama dengan penelitian ini, diantaranya adalah penelitian yang dilakukan oleh Wibowo dan Astuti (2021) [1], Nurhayati dkk. (2022) [2], Yirtici dan Yurtkan (2022) [3], dan Saiful dkk. (2022) [4].…”

Section: Pendahuluanunclassified

Real-Time Hand Gesture Detection and Recognition Using Convolutional Artificial Neural Networks

Prananta,

Azzikri,

Rozikin

2023

methodika

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Real-time hand gesture detection is an interesting topic in pattern recognition and computer vision. In this study, we propose the use of a Convolutional Neural Network (CNN) to detect and recognize hands in real-time. Our goal is to develop a system that can accurately identify and interpret user gestures in real-time. The proposed approach involves two main stages, namely hand gesture recognition and gesture recognition. For stage detection, we use the CNN architecture to recognize hands in the video. We train the CNN model using a dataset containing various hand gestures. Once a hand is detected, we extract the relevant hand region and proceed to the gesture recognition stage. The gesture recognition stage involves training and testing CNN models for different hand signal recognition. We use a hand gesture dataset that contains a variety of common hand signals. The experimental results show that the proposed system can detect and recognize hand movements in real-time with satisfactory accuracy. Although there are still some challenges that need to be overcome, this research provides a solid foundation for further development in real-time hand gesture recognition.

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Regional-CNN-based enhanced Turkish sign language recognition

Cited by 20 publications

References 10 publications

A machine learning-driven web application for sign language learning

A machine learning-driven web application for sign language learning

Exploring the use of computer vision in assistive technologies for individuals with disabilities: A review

Real-Time Hand Gesture Detection and Recognition Using Convolutional Artificial Neural Networks

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