[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126341-en":3,"doc-seo-126341-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126341,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Sign language recognition and classification using blended ensemble machine learning","An efficient sign language recognition system is proposed to address the challenge of simultaneous spatial–temporal modeling across multiple sources in dynamic signing. The approach begins with collecting an Indian sign language (ISL) dataset, followed by preprocessing using data augmentation and normalization. Segmentation is performed with a multi-threshold entropy function, then VGG-16 extracts features. Final classification uses ensemble machine learning, with performance validated via accuracy, precision, recall, and F1-score, achieving strong improvements over SVM and CNN baselines.","Sign language recognition and classification using blended  \nensemble machine learning  \nAkash Rajan Rai, Sujata Rajesh Kadu  \nDepartment of Information Technology, Terna College of Engineering, University of Mumbai, Navi Mumbai , India  \nArticle history:  \nReceived Mar 3, 2024 Revised Nov 25, 2024 Accepted Jan 27, 2025  \nKeywords:  \nBlended ensemble ML Data augmentation ISL recognition Multi-thresholding VGG-16  \nCorresponding Author:  \nAn efficient sign language recognition system (SLR) is the most significant for hearing-impaired people for communication. The body movements and hand gestures are utilized to characterize the vocabulary in dynamic sign language. The SLR is a challenging problem because the computational model requires simultaneous spatial-temporal modelling for a number of sources. To overcome this problem, this research proposes the blended ensemble machine learning (ML) approaches for SLR. Initially, the Indian sign language (ISL) dataset is collected for evaluating the effectiveness of the model. Then, the pre-processing is done by using data augmentation and normalization techniques. Then, the pre-processed data is provided to the segmentation process which is done by using multi-threshold entropy function. Then, VGG-16 is used for the feature extraction process to extract the features and finally, classification is carried out using ensemble ML. An effectiveness of the proposed method is validated based on accuracy, precision, recall, and F1-score, wherein it achieves better results of 99.57%, 0.92%, 0.95%, and 0.99% as compared to the existing works like support vector machine (SVM) and convolutional neural network (CNN) .  \nThis is an open access article under the CC BY-SA license.  \nAkash Rajan Rai  \nDepartment of Information Technology, Terna Engineering College, University of Mumbai Navi Mumbai, Maharashtra, India  \nEmail: [akashrai932@gmail.com](akashrai932@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nCommunication is significant for people to discover their necessities as well as interactions with other people. There are number of deaf and dumb people who majorly depend on sign language to communicate with others [1], [2] . The sign languages are measured as graphical and non-verbal form of communications utilized through differently-abled people to express themselves or interact with their surroundings. Globally, the sign language is the most emerging as well as challenging task. The sign language efficiently helps the hard-of-hearing as well as speed-impaired society in acquiring academic proficiency, professions as well as social rights [3], [4] . The sign language converts the words, sentences, numbers and letters of natural language to enable the vocally deactivated people to interact with the other people [5] . In sign language, the meaning and extraction of data is expressed through using hand gestures, movements of the body, facial expressions as well as emotions rather than sound, to send the messages. Moreover, sign language minimizes the communication gap among deaf and dumb people, facilitating smooth communication. The sign languages vary from region to region, and nation to nation [6], [7] . The number of researchers determine an exciting and exclusive form of communication in sign language over various nations. The machine learning (ML) and deep learning (DL) techniques have obtained better enhancement capabilities in sign language recognition (SLR) [8], [9] . The ML or DL techniques are  \nimplemented for the automatic recognition of sign language gestures to minimize communication gap with these people.  \nVarious researchers design the new approaches for SLR from the advantages of existing approaches to enhance the model’s performance [10], [11] . The SLR techniques are performed to enhance the efficiency of the model through minimizing the processing time, developing reliable databases, enabling quality enhancement. As an outcome, automatic SLR approaches are required to","cbCaiqN8zLN9ldZA","https://ap.wps.com/l/cbCaiqN8zLN9ldZA","pdf",493042,5,1,9,"English","en",105,"# Introduction\n## Problem background and motivation\n## Existing approaches and limitations\n## Proposed blended ensemble approach","[{\"question\":\"What problem does the proposed sign language recognition method target?\",\"answer\":\"It targets the difficulty of building a computational model that can capture simultaneous spatial–temporal information needed for dynamic sign language gesture recognition.\"},{\"question\":\"How is the ISL dataset used in the proposed pipeline?\",\"answer\":\"The method collects an Indian sign language dataset to evaluate the model, then applies preprocessing steps including data augmentation and normalization before segmentation.\"},{\"question\":\"Which techniques are used for segmentation, feature extraction, and classification?\",\"answer\":\"Segmentation uses a multi-threshold entropy function, feature extraction uses VGG-16, and final classification is carried out using ensemble machine learning.\"}]","Sign language recognition and classification using blended ensemble machine learning | 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problem does the proposed sign language recognition method target?","Question",{"text":77,"@type":78},"It targets the difficulty of building a computational model that can capture simultaneous spatial–temporal information needed for dynamic sign language gesture recognition.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How is the ISL dataset used in the proposed pipeline?",{"text":82,"@type":78},"The method collects an Indian sign language dataset to evaluate the model, then applies preprocessing steps including data augmentation and normalization before segmentation.",{"name":84,"@type":75,"acceptedAnswer":85},"Which techniques are used for segmentation, feature extraction, and classification?",{"text":86,"@type":78},"Segmentation uses a multi-threshold entropy function, feature extraction uses VGG-16, and final classification is carried out using ensemble machine 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