Sign Language Translator Using YOLO Algorithm

Bhavadharshini, M; J, Josephine Racheal; Kamali, Mohammad; S, Sankar Kumar

doi:10.3233/apc210136

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…They mentioned that by recognizing the occupancy of trained images, this system will arrange the input images. It can fulfill the predicted result of protest within bounding boxes [22].…”

Section: You Only Look Once(yolo)mentioning

confidence: 62%

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Computer Vision-Based Bengali Sign Language To Text Generation

Tazalli

Aunshu

Liya

et al. 2022

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)

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In the whole world, around 7% of people have hearing and speech impairment problems. They use sign language as their communication method. People from various countries use a variety of sign languages. As an example, there are ASL, CSL, JSL, etc. Even in our country, there are lots of people born with hearing and speech impairment problems. So, our primary focus is to work for those people by converting Bangla sign language into text. There are already various projects on Bangla sign language done by other people. However, they focused more on the separate alphabets and numerical numbers. That is why we want to concentrate on Bangla word signs since these people prefer to communicate using words or phrases rather than alphabets. There is not any proper database for Bangla word sign language, so we are making a database for Bangla word sign language for our work. In recognition of sign language (SLR), there usually are two types of scenarios: isolated SLR, which takes words by word and completes recognize action, and the other one is continuous SLR, which completes action by translating the whole sentence at once. We are working on isolated SLR. We introduce a method where we are going to use PyTorch and YOLOv5 for a video classification model to convert Bangla sign language into the text from the video where each video has only one sign language word. Here,we have achieved an accuracy rate of 76.29% on the training dataset and 51.44% on the testing dataset. We are working to build a system that will make it easier for hearing and speech-disabled people to interact with the general public.

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“…They mentioned that by recognizing the occupancy of trained images, this system will arrange the input images. It can fulfill the predicted result of protest within bounding boxes [22].…”

Section: You Only Look Once(yolo)mentioning

confidence: 62%

“…The author worked on building a system that works on ASL in real Time [22]. CNN with YOLO was used for this work.…”

Section: Chapter 2 Related Workmentioning

confidence: 99%

Computer Vision-Based Bengali Sign Language To Text Generation

Tazalli

Aunshu

Liya

et al. 2022

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)

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show abstract

“…It needs improvement in accuracy and the system only recognizes ASL. [14] proposed a system for real-time American Sign Language perception using a combination of Convolutional Neural Networks and the You Only Look Once (YOLO) algorithm. The system improved communication between hearing and hearing-impaired individuals by accurately recognizing and translating sign language gestures.…”

Section: Print Issn 2714-2469: E-issn 2782-8425 Uniosun Journal Of En...mentioning

confidence: 99%

Development of Mobile Application for Sign Language Translation Using Deep Learning Technique

Omodunbi,

Soladoye,

Okomba

et al. 2024

UJEES

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Sign Language is a visual and gestural language used by deaf and hard-of-hearing people to communicate. However, communication between hearing and non-hearing individuals can be challenging due to the language barrier. In order to overcome this barrier, a Mobile based Translator is being proposed using Convolutional Neural Network (CNN) to recognize and translate hand gestures in real-time. The proposed system consists of a CNN model trained on a large dataset of hand gestures that includes various signs, such as the alphabet, numbers, and common phrases to recognize various signs, and a backend server to handle the translation of the recognized signs into text. The system was implemented using various CNN architectures like ResNet, MobileNet and VGG-16, where the later gave the best accuracy of 97.69%. The trained VGG-16 model recognizes the signs by extracting features from the images of the hand gestures and using these features to classify the gestures. Once a gesture is recognized, the backend server translates it into text using a pre-defined mapping of signs to words or phrases. The translated text will then be displayed to the user in real-time on the mobile app, enabling seamless communication between hearing and non-hearing individuals. The proposed system was implemented as a mobile app using Flutter, which is a cross-platform development framework. This mobile app make communication easier for vulnerable and enable sharing of information without any discrimination.

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Real Time American Sign Language Recognition Using Yolov6 Model

Gomez,

Arzuaga

2024

Lecture Notes in Networks and Systems

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Sign Language Translator Using YOLO Algorithm

Cited by 9 publications

References 12 publications

Computer Vision-Based Bengali Sign Language To Text Generation

Computer Vision-Based Bengali Sign Language To Text Generation

Development of Mobile Application for Sign Language Translation Using Deep Learning Technique

Real Time American Sign Language Recognition Using Yolov6 Model

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