A Review on Sign Language Recognition Using CNN
- DOI
- 10.2991/978-94-6463-136-4_23How to use a DOI?
- Keywords
- Sign Language Recognition; Communication; Data Acquisition; Convolution Neural Network; Deep Learning
- Abstract
In sign language, hand gestures are used as one type of non-verbal communication. Individuals with hearing or speech problems typically use it to communicate with others or among themselves. Many makers around the world have created numerous sign language systems. The software that shows a system prototype capable of automatically recognizing sign language to assist deaf and dumb individuals in communicating with each other or regular people more successfully. The study demonstrates that there is ongoing research in the field of vision-based hand gesture recognition, with various studies being undertaken and a large number of publications appearing every year in journals and conference proceedings. Data acquisition, data environment, and hand gesture representation are the three main areas of concentration in publications on the hand gesture recognition system. In terms of recognition precision, we have also analyzed how well the recognition system performs. The recognition accuracy for the signer dependent spans from 69% to 98%, with an average of 88.8% among the chosen experiments.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Meena Ugale AU - Odrin Rodrigues Anushka Shinde AU - Kaustubh Desle AU - Shivam Yadav PY - 2023 DA - 2023/05/01 TI - A Review on Sign Language Recognition Using CNN BT - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022) PB - Atlantis Press SP - 251 EP - 259 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-136-4_23 DO - 10.2991/978-94-6463-136-4_23 ID - Ugale2023 ER -