TCN-ATTN-ISL: Decoding Signs with Vision and Memory
| dc.contributor.author | Maheshwari, Surbhi | |
| dc.contributor.author | Sinhal, Amit Kumar | |
| dc.date.accessioned | 2025-07-26T09:30:11Z | |
| dc.date.available | 2025-07-26T09:30:11Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Sign language is a vital medium for communication for many individuals who have a hearing impairment or speech deficiency. However, the limited channels in which it is integrated into the mainstream systems cause great impediments in personal as well as professional activities. This study proposes TCN-ATTNISL, An entirely novel framework for deep learning, real-time recognition of Indian Sign Language (ISL) gestures | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/6561 | |
| dc.language.iso | en | en_US |
| dc.subject | Decoding Signs | en_US |
| dc.title | TCN-ATTN-ISL: Decoding Signs with Vision and Memory | en_US |
| dc.type | Article | en_US |
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