We need a highly accurate deep-learning model that translates Indian Sign Language (ISL) sentence-level gestures into English sentences in real time. The key outcome is maximum translation accuracy with the lowest possible latency, and a design that remains practical and scalable after hand-off. Key Points: Input: Live video from a camera feed or landmark data from MediaPipe/OpenPose. Output: English word or phrase predictions rendered directly on a simple web page in real time. Platform: Web/Mobile platform deployment. Scope: Deliver a working proof-of-concept with clear, commented code, minimal UI, and easy-to-follow setup instructions so the system can be tested end to end. Model: Transformer-based architecture for sentence-level translation, optimized for real-time inference. Data: You may leverage publicly available Indian Sign Language sentence-level datasets. Deadline: ASAP — please outline a realistic schedule for an initial demo and final hand-off. Important Note: You do not need to train the model on the entire ISL sentence dataset. Training it on a small sample dataset (around 4–5 sentences) is sufficient. After you hand over the project with proper documentation and setup instructions, we will continue training it further from our end. Factors to consider: Practicality, Scalability, Accuracy, Performance, and Lowest Latency (as this will be a production-level model). If you have prior sign language projects — especially keypoint-based pipelines or sign-language translation models — please mention them; proven experience will be prioritized.