{"title":"基于python的机器学习应用程序，用于阿拉伯手语识别","authors":"Lamis Ali Hussein , Ziad Saeed Mohammed","doi":"10.1016/j.simpa.2025.100746","DOIUrl":null,"url":null,"abstract":"<div><div>The ArSLR-ML is a real-time interactive application that uses multi-class Support Vector Machines (SVM) machine learning applied in the classification procedure and MediaPipe in the feature extraction procedure to recognize static Arabic sign language gestures, focusing on numbers and letters and translating them into text and Arabic audio output. The ArSLR-ML was built within the PyCharm IDE using Python with a graphical user interface (GUI), thereby allowing for effective recognition of gestures. The application utilizes the laptop camera and GUI to capture hand gestures to create dataset for machine learning models and implement them in real time.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100746"},"PeriodicalIF":1.3000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ArSLR-ML: A Python-based machine learning application for arabic sign language recognition\",\"authors\":\"Lamis Ali Hussein , Ziad Saeed Mohammed\",\"doi\":\"10.1016/j.simpa.2025.100746\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The ArSLR-ML is a real-time interactive application that uses multi-class Support Vector Machines (SVM) machine learning applied in the classification procedure and MediaPipe in the feature extraction procedure to recognize static Arabic sign language gestures, focusing on numbers and letters and translating them into text and Arabic audio output. The ArSLR-ML was built within the PyCharm IDE using Python with a graphical user interface (GUI), thereby allowing for effective recognition of gestures. The application utilizes the laptop camera and GUI to capture hand gestures to create dataset for machine learning models and implement them in real time.</div></div>\",\"PeriodicalId\":29771,\"journal\":{\"name\":\"Software Impacts\",\"volume\":\"24 \",\"pages\":\"Article 100746\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2025-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Software Impacts\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2665963825000065\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software Impacts","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665963825000065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
ArSLR-ML: A Python-based machine learning application for arabic sign language recognition
The ArSLR-ML is a real-time interactive application that uses multi-class Support Vector Machines (SVM) machine learning applied in the classification procedure and MediaPipe in the feature extraction procedure to recognize static Arabic sign language gestures, focusing on numbers and letters and translating them into text and Arabic audio output. The ArSLR-ML was built within the PyCharm IDE using Python with a graphical user interface (GUI), thereby allowing for effective recognition of gestures. The application utilizes the laptop camera and GUI to capture hand gestures to create dataset for machine learning models and implement them in real time.
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