A hybrid approach for Bangla sign language recognition using deep transfer learning model with random forest classifier
作者：
Highlights：
• Demonstration of recent advancements in various Sign Language recognition research.
• Employment of proposed background elimination algorithm to remove unwanted features.
• Hybridization of transfer learning model with the Random Forest classifier.
• Employment of backbone networks pre-trained on ImageNet to handle smaller datasets.
• Improvement of evaluation parameters compared to other existing recognition systems.
摘要
•Demonstration of recent advancements in various Sign Language recognition research.•Employment of proposed background elimination algorithm to remove unwanted features.•Hybridization of transfer learning model with the Random Forest classifier.•Employment of backbone networks pre-trained on ImageNet to handle smaller datasets.•Improvement of evaluation parameters compared to other existing recognition systems.
论文关键词：Bangla sign language,Deep learning,Convolutional neural network (CNN),Transfer learning,Character recognition,Digit recognition
论文评审过程：Received 2 June 2022, Revised 15 August 2022, Accepted 24 September 2022, Available online 30 September 2022, Version of Record 17 October 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118914
