[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122217-en":3,"doc-seo-122217-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122217,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",6,"Technology","Automated Sign Language Recognition with Machine Learning - System for Real-Time Gesture Interpretation","The research designs a machine learning system to automate sign language gesture recognition and reduce communication barriers for people who are deaf or mute. A computer vision pipeline recognizes hand gestures and maps them to text or speech using a dataset of static American Sign Language (ASL) hand signs. Convolutional Neural Networks (CNNs) are used for feature extraction and gesture classification, achieving high accuracy and reliable generalization. A Python Tkinter interactive real-time interface processes webcam frames and displays the identified characters with minimal delay.","International Journal of Innovative Research in Science  \nEngineering and Technology (IJIRSET)  \n(A Monthly, Peer Reviewed, Refereed, Scholarly Indexed, Open Access Journal)  \nImpact Factor: 8.699 Volume 14, Issue 4 , April 2025  \n|[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|  \nVolume 14, Issue 4, April 2025  \n|DOI: 10.15680/IJIRSET.2025.1404454|  \nAutomated Sign Language Recognition with  \nMachine Learning  \nPrashanthi Regonda, Eppili Jatin, Attem Varun Yadav, Javadi Adarsh Kumar  \nAssociate Professor, Department of Computer Engineering, Sreenidhi Institute of Science and Technology, JNTUH,  \nHyderabad, India  \nU.G. Student, Department of Computer Engineering, Sreenidhi Institute of Science and Technology, JNTUH,  \nHyderabad, India  \nU.G. Student, Department of Computer Engineering, Sreenidhi Institute of Science and Technology, JNTUH,  \nHyderabad, India  \nU.G. Student, Department of Computer Engineering, Sreenidhi Institute of Science and Technology, JNTUH,  \nHyderabad, India  \nABSTRACT: The purpose of this research is to design a system using machine learning to automate sign language gesture recognition and facilitate unproblematic interaction between the hearing/speech impaired community and society as a whole. This research works towards solving the communication disability faced by the deaf and mute community through a computer vision framework for recognizing hand gestures and mapping them to relevant text or speech. The work employs a rich dataset of static American Sign Language (ASL) hand signs and uses Convolutional Neural Networks (CNNs) for efficient feature extraction and gesture classification. The suggested model is trained and evaluated on a huge collection of labeled gesture images and exhibits high accuracy with robust generalization to novel data. One of the primary contributions of the work is the implementation of the trained model into an interactive, realtime graphical user interface developed with Python's Tkinter library. The interface captures live input from a webcam, processes each frame to identify gestures, and shows the identified character on the screen. The results of the evaluation confirm the ability of CNNs to accurately identify ASL alphabets with little delay. The proposed system presents an effective and scalable approach towards real-time sign language interpretation, supporting inclusiveness and accessibility of the disabled community. The present research also creates a scope for extension in development, i.e., inclusion of dynamic gesture detection and portability to mobile or embedded platforms to provide enhanced use in real-life situations.  \nKEYWORDS: Sign Language Recognition, Convolutional Neural Networks, American Sign Language, Real-Time Gesture Detection, Human-Computer Interaction  \nI. INTRODUCTION  \nSign language is the primary means of communication for individuals with speech and hearing impairments. With limited public proficiency, they often rely on interpreters, who are not always available or affordable. An automated Sign Language Recognition (SLR) system would be able to effectively translate gestures, facilitating communication barriers[1] and improving social integration. SLR systems require huge data to translate sign languages appropriately, which consist of extremely intricate regional variation of grammar and vocabulary. It becomes difficult to implement a global model because of its complexity, and localized SLR models are more effective. SLR systems transform HumanComputer Interaction using gesture recognition, bridging sign language speakers to society. Machine learning methods for creating these systems are presented in this paper.  \nII. INTELLIGENT SIGN LANGUAGE RECOGNITION USING MACHINE LEARNING  \nAn intelligent Sign Language Recognition (SLR) system identifies hand gestures, facial expressions, and posture to interpret signs. As a signer s","cbCaijmn7Otqda2h","https://ap.wps.com/l/cbCaijmn7Otqda2h","pdf",1331000,1,9,"English","en",105,"# Introduction\n# Intelligent Sign Language Recognition Using Machine Learning\n## Preprocessing","[{\"question\":\"What problem does the automated sign language recognition system address?\",\"answer\":\"It targets communication disabilities by translating sign language gestures, helping bridge interaction between deaf/mute users and society.\"},{\"question\":\"Which machine learning technique is used for gesture recognition?\",\"answer\":\"The work uses Convolutional Neural Networks (CNNs) to extract features and classify gestures from labeled image data.\"},{\"question\":\"How does the system work in real time?\",\"answer\":\"A Python Tkinter graphical interface captures live webcam input, processes each frame to identify gestures, and displays the recognized character on screen with little delay.\"}]","Automated Sign Language Recognition with Machine Learning - System for Real-Time Gesture Interpretation | PDF",1785809422,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"automated-sign-language-recognition-with-machine-learning-system-for-real-time-gesture-interpretation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/automated-sign-language-recognition-with-machine-learning-system-for-real-time-gesture-interpretation/122217/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the automated sign language recognition system address?","Question",{"text":75,"@type":76},"It targets communication disabilities by translating sign language gestures, helping bridge interaction between deaf/mute users and society.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning technique is used for gesture recognition?",{"text":80,"@type":76},"The work uses Convolutional Neural Networks (CNNs) to extract features and classify gestures from labeled image data.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the system work in real time?",{"text":84,"@type":76},"A Python Tkinter graphical interface captures live webcam input, processes each frame to identify gestures, and displays the recognized character on screen with little delay.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]