Abrantepah/SignTalk-SignLMM

A Multimodal Large Model (LMM) for Ghanaian Sign Language (GSL) recognition, using deep learning to interpret sign language from video. It integrates vision and language models for improved accessibility. Built with PyTorch, Transformers, OpenCV, and Mediapipe. 🚀 # **SignTalk-SignLMM** **Multimodal Large Model for Ghanaian Sign Language Recognition and Understanding** SignTalk-SignLMM is a research-driven project focused on building a **Large Multimodal Model (LMM)** for **Ghanaian Sign Language (GSL) recognition and understanding**. Leveraging video datasets, deep learning, and AI-driven sign language processing, this project aims to bridge communication gaps for the Deaf community. ## **Key Features** ✅ **Sign Language Recognition** – Detect and interpret GSL signs from video inputs. ✅ **Multimodal Learning** – Combine vision and language models for better sign comprehension. ✅ **Dataset & Model Training** – Train deep learning models on a curated GSL dataset. ✅ **Responsible AI** – Ensure fairness, inclusivity, and real-world usability. ## **Tech Stack** 🔹 **Python, PyTorch, TensorFlow** – Deep Learning & Model Training 🔹 **OpenCV, Mediapipe** – Video Preprocessing & Hand Tracking 🔹 **Transformers, CLIP, LLaVA** – Multimodal AI for Sign Language Understanding 🔹 **Flutter, Flask, FastAPI** – Potential Deployment & API Support ## **Project Goals** 🚀 Develop an efficient **Sign Language Model** for Ghanaian Sign Language 📊 **Collect & Annotate Data** to train models effectively 🔍 **Enhance Sign Language AI** with multimodal learning 🌍 Promote **Accessibility & Inclusion** through AI