A Comprehensive Review on Vision-based Sign Language Detection and Recognition

dc.contributor.authorWeerasinghe, R. L.
dc.contributor.authorGanegoda, G. U.
dc.date.accessioned2022-10-31T08:46:29Z
dc.date.available2022-10-31T08:46:29Z
dc.date.issued2022
dc.description.abstractDeaf or hard-hearing people's primary mode of communication is sign language. Communication between hard hearing and hearing people is greatly aided by sign language recognition technologies. With the advent of technology, many approaches proposed for sign language recognition. Among them, vision-based approaches are more convenient than sensor-based approaches. Vision-based approaches are involved five different stages where various techniques and algorithms are utilized in various approaches. The accuracy of the recognition is based on the techniques used and the quality of the input. Background invariance and lighting conditions highly affect the accuracy of the result. Simply by increasing the quality of the input, each and every method can approach a high accuracy rate. This paper provides a comprehensive introduction and comparison of the existing vision-based sign language detection and recognition approaches.en_US
dc.identifier.citationWeerasinghe R. L.; Ganegoda G. U. (2022), A Comprehensive Review on Vision-based Sign Language Detection and Recognition, International Research Conference on Smart Computing and Systems Engineering (SCSE 2022), Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka. 88-95.en_US
dc.identifier.urihttp://repository.kln.ac.lk/handle/123456789/25407
dc.publisherDepartment of Industrial Management, Faculty of Science, University of Kelaniya Sri Lankaen_US
dc.subjectANN, computer vision, gesture recognition, image processing, sign languageen_US
dc.titleA Comprehensive Review on Vision-based Sign Language Detection and Recognitionen_US

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
SCSE 2022 14.pdf
Size:
13.76 KB
Format:
Adobe Portable Document Format
Description:

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: