Project Title: Build a Real-Time Indian Sign Language (ISL) Recognition Model in 1–2 Days (Words, Characters, Numbers) Project Description: I need a machine learning engineer to quickly develop a trained model capable of detecting Indian Sign Language (ISL) gestures, specifically words, characters (A-Z), and numbers (0-9), from live video input. The model should be capable of recognizing these gestures in real-time (within 100ms latency). Key Features: Character Detection (A–Z): Recognize hand gestures representing the 26 English alphabets in Indian Sign Language (ISL). Number Recognition (0–9): Recognize numbers represented in ISL (0–9). Word Recognition: Recognize common Indian Sign Language (ISL) words like hello, thank you, sorry, please, yes, no, love, you, me, and others. Mixed Gesture Sequences: The model should recognize and output mixed gestures like: "I love you 3000" → "I" (character) + "love" (word) + "you" (word) + "3000" (number). Real-Time Detection: The model should be optimized for real-time use (e.g., < 100ms detection latency) for live webcam input. Deliverables: Trained Model: A trained machine learning model capable of detecting alphabets, numbers, and common words in ISL. Model export formats: .h5, .onnx, .tfjs (for web). Dataset: If the dataset is not already available, provide guidance on how to quickly collect and label a small dataset of hand gestures (e.g., A-Z, 0-9, and common words). At least 100-200 samples per gesture would be sufficient to start. Preprocessing Code: Code to preprocess video frames, including hand detection and feature extraction (e.g., hand landmarks, image normalization). Real-Time Inference Code: Python or TensorFlow.js code that processes webcam video in real-time and uses the trained model for gesture recognition. Code should include token detection, sentence building, and displaying the recognized text. Documentation: A basic README explaining the model architecture, how to run the inference code, and usage instructions. Required Skills: Machine Learning (ML) with experience in computer vision and gesture recognition. Deep Learning frameworks such as TensorFlow, Keras, or PyTorch. Familiarity with real-time gesture recognition models (e.g., MediaPipe, HandPose, OpenCV). Experience with TensorFlow.js for web-based deployment is a plus. Ability to work under tight deadlines and deliver working code quickly. Preferred Experience: Previous work on gesture recognition, sign language, or pose estimation models. Knowledge of real-time video processing and model optimization for speed. Timeline: 1-2 Days: This is a time-sensitive project. The goal is to have a prototype or MVP delivered within 1-2 days. Quick Turnaround: Please provide an estimate based on this aggressive timeline. Budget: The budget is negotiable based on your expertise and quick turnaround. Please provide an estimated cost for the entire project along with a detailed breakdown of tasks. How to Apply: Please include: A brief summary of your relevant experience in gesture recognition and real-time models. Your proposed approach to building the model and meeting the deadline. An estimated cost and timeline (with consideration for the 1-2 day requirement). Examples of similar projects or portfolios (if available). Additional Notes: If you have a pre-existing dataset or can quickly adapt an existing one for ISL, please mention it. The project will require real-time inference, so please optimize for low latency (less than 100 ms). Looking forward to your proposals!