Sign language recognition using deep learning through LSTM and CNN
This study presents the application of using deep learning to detect, recognize and translate sign language. Understanding sign language is crucial for communication between the deaf and mute people and the general society. This helps sign language users to easily communicate with others, thus elimi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38079/ http://umpir.ump.edu.my/id/eprint/38079/1/Sign%20Language%20Recognition%20using%20Deep%20Learning%20through%20LSTM%20and%20CNN.pdf |
| _version_ | 1848825417264267264 |
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| author | Kiran, Pandian Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Muhammad Amirul, Abdullah Ahmad Fakhri, Ab. Nasir Wan Hasbullah, Mohd Isa |
| author_facet | Kiran, Pandian Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Muhammad Amirul, Abdullah Ahmad Fakhri, Ab. Nasir Wan Hasbullah, Mohd Isa |
| author_sort | Kiran, Pandian |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This study presents the application of using deep learning to detect, recognize and translate sign language. Understanding sign language is crucial for communication between the deaf and mute people and the general society. This helps sign language users to easily communicate with others, thus eliminating the differences between both parties. The objectives of this thesis are to extract features from the dataset for sign language recognition model and the formulation of deep learning models and the classification performance to carry out the sign language recognition. First, we develop methodology for an efficient recognition of sign language. Next is to develop multiple system using three different model which is LSTM, CNN and YOLOv5 and compare the real time test result to choose the best model with the highest accuracy. We used same datasets for all algorithms to determine the best algorithm. The YOLOv5 has achieved the highest accuracy of 97% followed by LSTM and CNN with 94% and 66.67%. |
| first_indexed | 2025-11-15T03:28:35Z |
| format | Article |
| id | ump-38079 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:28:35Z |
| publishDate | 2023 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-380792023-07-20T01:28:04Z http://umpir.ump.edu.my/id/eprint/38079/ Sign language recognition using deep learning through LSTM and CNN Kiran, Pandian Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Muhammad Amirul, Abdullah Ahmad Fakhri, Ab. Nasir Wan Hasbullah, Mohd Isa QA76 Computer software TJ Mechanical engineering and machinery This study presents the application of using deep learning to detect, recognize and translate sign language. Understanding sign language is crucial for communication between the deaf and mute people and the general society. This helps sign language users to easily communicate with others, thus eliminating the differences between both parties. The objectives of this thesis are to extract features from the dataset for sign language recognition model and the formulation of deep learning models and the classification performance to carry out the sign language recognition. First, we develop methodology for an efficient recognition of sign language. Next is to develop multiple system using three different model which is LSTM, CNN and YOLOv5 and compare the real time test result to choose the best model with the highest accuracy. We used same datasets for all algorithms to determine the best algorithm. The YOLOv5 has achieved the highest accuracy of 97% followed by LSTM and CNN with 94% and 66.67%. Penerbit UMP 2023-04 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/38079/1/Sign%20Language%20Recognition%20using%20Deep%20Learning%20through%20LSTM%20and%20CNN.pdf Kiran, Pandian and Mohd Azraai, Mohd Razman and Ismail, Mohd Khairuddin and Muhammad Amirul, Abdullah and Ahmad Fakhri, Ab. Nasir and Wan Hasbullah, Mohd Isa (2023) Sign language recognition using deep learning through LSTM and CNN. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 5 (1). pp. 67-71. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v5i1.9410 https://doi.org/10.15282/mekatronika.v5i1.9410 |
| spellingShingle | QA76 Computer software TJ Mechanical engineering and machinery Kiran, Pandian Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Muhammad Amirul, Abdullah Ahmad Fakhri, Ab. Nasir Wan Hasbullah, Mohd Isa Sign language recognition using deep learning through LSTM and CNN |
| title | Sign language recognition using deep learning through LSTM and CNN |
| title_full | Sign language recognition using deep learning through LSTM and CNN |
| title_fullStr | Sign language recognition using deep learning through LSTM and CNN |
| title_full_unstemmed | Sign language recognition using deep learning through LSTM and CNN |
| title_short | Sign language recognition using deep learning through LSTM and CNN |
| title_sort | sign language recognition using deep learning through lstm and cnn |
| topic | QA76 Computer software TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/38079/ http://umpir.ump.edu.my/id/eprint/38079/ http://umpir.ump.edu.my/id/eprint/38079/ http://umpir.ump.edu.my/id/eprint/38079/1/Sign%20Language%20Recognition%20using%20Deep%20Learning%20through%20LSTM%20and%20CNN.pdf |