[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118949-en":3,"doc-seo-118949-105":30,"detail-sidebar-cat-0-en-105":92},{"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},118949,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",6,"Technology","GESTURE RECOGNITION OF SIGN LANGUAGE ALPHABET USING MACHINE LEARNING TECHNIQUES - A Project Report","With the rising incidence of hearing loss, effective sign language recognition has become crucial for enhancing communication for individuals with hearing impairments. Traditional sensor-based recognition systems have been challenged by the complexities of real-world settings, prompting a shift toward more adaptable vision-based recognition systems. Using the Sign Language MNIST dataset, the work evaluates ensemble methods combined with advanced filtering techniques. Filters such as Sobel, Canny, and Hough transform are applied in preprocessing for CNN, XGBoost, LightGBM, CatBoost, SVM, and VGG16, analyzing accuracy, training time, prediction speed, and trade-offs.","San Jose State University  \nSJSU ScholarWorks  \n\n| Master's Projects | Theses and Graduate Research |\n| --- | --- |\n| Fall 2023\u003Cbr>GESTURE RECOGNITION OF SIGN LANGUAGE ALPHABET USING MACHINE LEARNING TECHNIQUES\u003Cbr>Gursimran Singh\u003Cbr>Follow this and additional works at: [https://scholarworks.sjsu.edu/etd_projects](https://scholarworks.sjsu.edu/etd_projects)\u003Cbr> Part of the Other Computer Engineering Commons |  |\n\nRecommended Citation  \nSingh, Gursimran, \"GESTURE RECOGNITION OF SIGN LANGUAGE ALPHABET USING MACHINE LEARNING TECHNIQUES\" (2023) . Master 's Projects. 1325.  \nDOI: [https://doi.org/10.31979/etd.w29m-5xh2](https://doi.org/10.31979/etd.w29m-5xh2)  \n[https://scholarworks.sjsu.edu/etd_projects/1325](https://scholarworks.sjsu.edu/etd_projects/1325)  \nThis Master's Project is brought to you for free and open access by the Theses and Graduate Research at SJSU ScholarWorks. It has been accepted for inclusion in Master's Projects by an authorized administrator of SJSU ScholarWorks. For more information, please contact [scholarworks@sjsu.edu](scholarworks@sjsu.edu).  \nGESTURE RECOGNITION OF SIGN LANGUAGE ALPHABET USING  \nMACHINE LEARNING TECHNIQUES  \nA Project Report  \nPresented to  \nThe Faculty of Department of Computer Science  \nSan José State University  \nIn Partial Fulfilment  \nOf the Requirements for Degree  \nMaster of Data Science  \nby  \nGursimran Singh  \n© 2023  \nGursimran Singh  \nALL RIGHTS RESERVED  \nThe Designated Project Committee Approves the Project Titled  \nGESTURE RECOGNITION OF SIGN LANGUAGE ALPHABET  \nUSING MACHINE LEARNING TECHNIQUES  \nby  \nGursimran Singh  \nAPPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE  \nSAN JOSÉ STATE UNIVERSITY Dec 2023  \nDr. Ching-Seh Wu, Ph.D  \nDr. Navarati Saxena  \nDr. Fabio di Troia  \nDepartment of Computer Science  \nDepartment of Computer Science  \nDepartment of Computer Science  \nABSTRACT  \nGESTURE RECOGNITION OF SIGN LANGUAGE  \nALPHABET USING MACHINE LEARNING TECHNIQUES by-Gursimran Singh  \nWith the rising incidence of hearing loss, effective sign language recognition has become crucial for enhancing communication for individuals with hearing impairments. Traditional sensor-based recognition systems have been challenged by the complexities of realworld settings, prompting a shift toward more adaptable vision-based recognition systems. Distinct from previous studies, this work pioneers the use of ensemble methods with advanced filtering techniques on the Sign Language MNIST dataset, offering a novel perspective on sign language recognition. This research delves into the intersection of machine learning and image processing to develop a robust framework for sign language recognition. A range of filters, including Sobel, Canny, and Hough transform, were employed in preprocessing to optimize feature extraction across various machine learning models such as Convolutional Neural Networks (CNN), XGBoost, Light GBM, CatBoost, Support Vector Machines (SVM), and VGG16 . Our findings reveal that while the SVM and XGBoost models require considerable training time due to the image-based data's complexity, the ensemble models, particularly those pairing CNN with SVM, XGBoost, Light GBM, and CatBoost, exhibit a synergistic effect that balances training efficiency with high accuracy. The Sobel filter with SVM ensemble emerged as the most rapid in training, indicating its potential for real-time applications. Accuracy assessments demonstrated that CNN and CNN+SVM models achieved perfect scores, signifying their exceptional categorization capabilities, whereas models like VGG 16 need refinement to mitigate overfitting or dataset-related limitations. The predictive efficiency of these models was systematically classified, with some exhibiting remarkably swift prediction times, underscoring their suitability for systems where computational resources are a constraint. Ultimately, this study comprehensively evaluates various machine learning models, highlighting the trade-offs between computational dema","cbCaieumfMDwhgly","https://ap.wps.com/l/cbCaieumfMDwhgly","pdf",4589645,1,83,"English","en",105,"# Table of Contents\n## Introduction\n## Literature Review","[{\"question\":\"Why is sign language gesture recognition important in this project?\",\"answer\":\"The project motivates recognition as crucial for improving communication for individuals with hearing impairments as hearing loss becomes more prevalent.\"},{\"question\":\"What dataset and preprocessing approach does the study use?\",\"answer\":\"The study uses the Sign Language MNIST dataset and applies image preprocessing filters such as Sobel, Canny, and Hough transform to improve feature extraction.\"},{\"question\":\"Which models and filter combinations performed best, and what trade-offs were observed?\",\"answer\":\"The CNN and CNN+SVM models achieved perfect accuracy, while ensemble pairings like CNN with SVM, XGBoost, LightGBM, and CatBoost showed a balance of training efficiency and accuracy. Sobel with an SVM ensemble produced the fastest training, and some models like VGG16 required refinement to reduce overfitting or dataset-related issues.\"}]","GESTURE RECOGNITION OF SIGN LANGUAGE ALPHABET USING MACHINE LEARNING TECHNIQUES - A Project Report | PDF",1785721150,209,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"gesture-recognition-of-sign-language-alphabet-using-machine-learning-techniques-a-project-report","",{"@graph":36,"@context":86},[37,54,69],{"@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/gesture-recognition-of-sign-language-alphabet-using-machine-learning-techniques-a-project-report/118949/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is sign language gesture recognition important in this project?","Question",{"text":76,"@type":77},"The project motivates recognition as crucial for improving communication for individuals with hearing impairments as hearing loss becomes more prevalent.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What dataset and preprocessing approach does the study use?",{"text":81,"@type":77},"The study uses the Sign Language MNIST dataset and applies image preprocessing filters such as Sobel, Canny, and Hough transform to improve feature extraction.",{"name":83,"@type":74,"acceptedAnswer":84},"Which models and filter combinations performed best, and what trade-offs were observed?",{"text":85,"@type":77},"The CNN and CNN+SVM models achieved perfect accuracy, while ensemble pairings like CNN with SVM, XGBoost, LightGBM, and CatBoost showed a balance of training efficiency and accuracy. Sobel with an SVM ensemble produced the fastest training, and some models like VGG16 required refinement to reduce overfitting or dataset-related issues.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,114,119,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]