{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/real-time-sign-language-fingerspelling","title":"Real-time Sign Language Fingerspelling Recognition using Convolutional Neural Networks from Depth map","arxiv_id":"1509.03001","date":"2015-09-10","proceeding":null,"authors":["Byeongkeun Kang","Subarna Tripathi","Truong Q. Nguyen"],"abstract":"Sign language recognition is important for natural and convenient\ncommunication between deaf community and hearing majority. We take the highly\nefficient initial step of automatic fingerspelling recognition system using\nconvolutional neural networks (CNNs) from depth maps. In this work, we consider\nrelatively larger number of classes compared with the previous literature. We\ntrain CNNs for the classification of 31 alphabets and numbers using a subset of\ncollected depth data from multiple subjects. While using different learning\nconfigurations, such as hyper-parameter selection with and without validation,\nwe achieve 99.99% accuracy for observed signers and 83.58% to 85.49% accuracy\nfor new signers. The result shows that accuracy improves as we include more\ndata from different subjects during training. The processing time is 3 ms for\nthe prediction of a single image. To the best of our knowledge, the system\nachieves the highest accuracy and speed. The trained model and dataset is\navailable on our repository.","url_abs":"http://arxiv.org/abs/1509.03001v3","url_pdf":"http://arxiv.org/pdf/1509.03001v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"real-time-sign-language-fingerspelling","repo_url":"https://github.com/byeongkeun-kang/FingerspellingRecognition","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"sign-language-recognition","task_name":"Sign Language Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}