Build a Real-Time Indian Sign Language (ISL) Recognition Model
Budget: ₹1,500 – ₹12,500 INR
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!
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!