Mark Christian Ang, Karl Richmond C. Taguibao, C. O. Manlises
{"title":"Hand Gesture Recognition for Filipino Sign Language Under Different Backgrounds","authors":"Mark Christian Ang, Karl Richmond C. Taguibao, C. O. Manlises","doi":"10.1109/IICAIET55139.2022.9936801","DOIUrl":null,"url":null,"abstract":"The article implements a hand gesture Filipino Sign Language recognition model using Raspberry Pi. Numerous studies on Filipino Sign Language (FSL) frequently identify a letter with a glove and using a plain background, which may be challenging if implemented in a more complex background. Limited research on the implementation of YOLO-Lite and MobileNetV2 on FSL were also observed. Implementing YOLO-Lite for hand detection and MobileNetV2 for classification, the average accuracy achieved for differentiating 26 hand gestures, representing FSL letters, was 93.29%. The model demonstrated dependability in a variety of complex backgrounds. However, challenges in recognizing letters Q, J, and Z were encountered. Additionally, in letters N and M, due to their similar hand structures, N is sometimes mistakenly interpreted as M. The model developed by the researchers performed well and demonstrated better accuracy compared to a different model. The system was able to achieve higher accuracy while running on limited resources and in various environments.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IICAIET55139.2022.9936801","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The article implements a hand gesture Filipino Sign Language recognition model using Raspberry Pi. Numerous studies on Filipino Sign Language (FSL) frequently identify a letter with a glove and using a plain background, which may be challenging if implemented in a more complex background. Limited research on the implementation of YOLO-Lite and MobileNetV2 on FSL were also observed. Implementing YOLO-Lite for hand detection and MobileNetV2 for classification, the average accuracy achieved for differentiating 26 hand gestures, representing FSL letters, was 93.29%. The model demonstrated dependability in a variety of complex backgrounds. However, challenges in recognizing letters Q, J, and Z were encountered. Additionally, in letters N and M, due to their similar hand structures, N is sometimes mistakenly interpreted as M. The model developed by the researchers performed well and demonstrated better accuracy compared to a different model. The system was able to achieve higher accuracy while running on limited resources and in various environments.