Sign Bridge
| dc.contributor.author | Ahsaan Munir, Rimsha Tariq and Muneeb Ahmad | |
| dc.date.accessioned | 2025-08-25T09:54:28Z | |
| dc.date.available | 2025-08-25T09:54:28Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | The Sign Bridge is an AI-powered solution developed to bridge the critical communication gap between individuals with hearing impairments and the general public in Pakistan. This system focuses on recognizing and interpreting gestures from USL and converting them into readable Urdu text. By leveraging advanced techniques in computer vision, deep learning, and gesture recognition, the project contributes meaningfully to inclusive technology and accessible communication. At the core of the system is a combination of Convolutional Neural Networks (CNNs) for image-based feature extraction, and MediaPipe for real-time hand landmark detection. The model is trained on a custom-built dataset comprising 54 frequently used Urdu sign language words, each recorded through 20 short-duration videos. This dataset provides the foundational training data for model learning and testing. Despite its relatively modest size, the model achieved 55% accuracy, proving the feasibility of real-time Urdu sign language interpretation using a lightweight, scalable approach. | |
| dc.identifier.uri | https://escholar.umt.edu.pk/handle/123456789/5272 | |
| dc.language.iso | en | |
| dc.publisher | UMT, Lahore | |
| dc.title | Sign Bridge | |
| dc.type | Thesis |