Indian Sign Language Recognition Model Development

@dushyant317

I bring 13 years of professional experience delivering high-quality results. I have strong expertise in all the required skills listed for this project. My approach ensures accuracy, clear communication, and timely delivery. I am confident I can exceed your expectations with efficient, reliable work. Looking forward to contributing to your project—ready to begin immediately.

@duncank52

Hi there, From your description, this looks like the kind of projects like this where a expert senior engineer approach actually matters. Here’s how I’d approach it: With Python, Algorithm, CUDA in your stack, I’ll keep the structure lightweight, predictable and easy to maintain. • Surface edge cases early and handle them deliberately instead of reacting last minute. • Break the work into simple stages so you see progress early, not just at the end. We can also start with a small initial slice so you can see the quality before we run the full project. If this feels aligned with what you’re aiming for, I’d be glad to take this forward. Best regards, Duncan.

@yogeshjoshi9

Hi, What specific gestures do you want to start with for the model? It sounds like a fascinating project! I can build a simple machine learning model using TensorFlow/Keras that recognizes ISL gestures for characters, numbers, and a few words. To achieve real-time recognition, I can utilize MediaPipe for accurate gesture detection. The model will process webcam input and provide instant text output, ensuring it responds within 100ms. I can also help you with dataset creation and labeling, if needed. I have experience in gesture recognition and have worked on similar projects. Given the timeline, I can deliver a working prototype within 1–2 days for an estimated budget of around $150. Let’s get started on this impactful project! Y

@usamaansari786

Hi, I understand your requirements. As an AI expert and a Python pro, I am well-equipped to handle your project. I have hands-on experience with TensorFlow and Keras, which are essential for creating powerful machine learning models such as the one you require. Additionally, my familiarity with MediaPipe and HandPose will prove useful in implementing the Gesture Recognition and Hand Pose Estimation aspects of this particular project. Utilizing my knowledge in real-time video processing, I am confident in providing a model that delivers on your key features: recognizing characters (A-Z), numbers (0-9), and common words in Indian Sign Language. My approach would prioritize speed without compromising on accuracy, ensuring that your model can process webcam videos quickly (under 100ms) - all within the short timeframe you need. Lets have a chat warm regards Usama Ansari

@abhinayk67

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@ayesha86664

Hi there! I see you need a simple Indian Sign Language recognition model, and building it quickly without prior experience can be tricky. I can develop a lightweight ML model to recognize A-Z characters, 0-9 numbers, and basic ISL words in real-time (<100ms) using TensorFlow and MediaPipe for hand detection. I’ll provide the trained model (.h5 or ONNX), a Python inference script for webcam input, optional text-to-speech output, and clear documentation for setup and testing. Guidance on collecting a small dataset will also be included if needed. Do you want the model to include live text-to-speech output along with the recognized gestures? Start /Open chat now

@rossl20

Hello, I can build your simple Indian Sign Language (ISL) recognition prototype (A–Z, 0–9, and basic words) within the requested 1–2 day timeline using fast, lightweight ML models optimized for real-time performance. ✔ My Approach (Fast & Practical for 1–2 Days) 1. Use MediaPipe Hands for Keypoint Extraction (Fast & Accurate) • Extract 21 hand landmarks (x, y, z) per frame. • Avoids the need for thousands of training images. • Gives stable detection even in low-light or varying backgrounds. 2. Train a Lightweight Classifier • Train a small model (Keras or PyTorch) on landmark coordinates, not images. • This reduces training time dramatically and works perfectly for MVP. 3. Classes Covered • A–Z (26 classes) • 0–9 (10 classes) • Basic Words: hello, thank you, sorry, yes, no, love (up to 6 classes) Total classes: ~42 Latency target: 40–80ms per frame (achievable). If needed, I can also generate a starter dataset template. ✔ Estimated Cost Since this is a small MVP with basic accuracy and fast delivery, I can complete the project at an affordable rate. Estimated Cost: ₹600 I keep the cost low because this is a prototype, not a production-level system. Just let me know and I’ll show previous work. I’m ready to start immediately and deliver a smooth, functional ISL recognition system in real time. Looking forward to working with you!

@deepl55

Hello, My Approach • Use MediaPipe Hands to extract highly accurate 21-point hand landmarks. • Build a lightweight TensorFlow/Keras classifier (MLP or small CNN) that can recognize ISL alphabets (A–Z), numbers (0–9), and basic words such as hello, thank you, sorry, yes, no, love. • Ensure real-time performance by using landmark-based classification rather than heavy image CNNs. • Provide real-time inference through a Python OpenCV script that reads webcam input, processes gestures, and displays the recognized text. • Optional: Add text-to-speech output using gTTS or browser Web Speech API. • Deliver the model in .h5 or ONNX format, along with a clean and easy-to-follow README. What You Will Receive • A trained ISL recognition model (A–Z, 0–9, basic words). • Real-time inference script with webcam support. • Guidance or tools for collecting and labeling gesture data (100–200 samples per class). • Clear documentation on running, testing, and extending the system. • A minimal but functional prototype within the requested timeframe. Why I’m Confident I Can Deliver I’ve worked on gesture-based recognition pipelines before, using MediaPipe, TensorFlow, OpenCV, and real-time ML systems. I focus on building fast, lightweight, and practical models rather than heavy experimental architectures, which makes it realistic to deliver a strong MVP quickly. If you’d like me to proceed, I can start immediately and deliver a clean, working prototype within your 1–2 day timeline.

@pressleyw

Hello, With a strong foundation in computer science and an especial focus on artificial intelligence and deep learning, I am confident in my ability to tackle your ISL recognition project. My skills in Python, TensorFlow, and other relevant technologies align impeccably with the requirements of this assignment. Furthermore, my familiarity with real-time processing and asset such as MediaPipe and HandPose will expedite development while ensuring an end result that meets your needs. Though the task timeline is tight, as a seasoned professional I've become adept at delivering quality solutions within strict timeframes without compromising on accuracy or efficiency. I guarantee that you'll have a working prototype or minimum viable product that precisely meets your outlined specifications – recognizing simple hand gestures to display or vocalize the corresponding text – within the requested 1 to 2 days window. In terms of pricing, rest assured my rates are competitive while still acknowledging that quality work warrants equitable remuneration. Even with a tight timeline And limited budget considerations, I'm able to provide accurate estimates before commencing any project. So let's get started; I look forward to bringing your vision from ideation to reality efficiently and effectively. Thanks!

@romanpotapcz

Dear I am excited to help you build a simple real-time Indian Sign Language (ISL) recognition model for characters (A-Z), numbers (0-9), and basic words like “hello,” “thank you,” “sorry,” “yes,” “no,” and “love.” Proposed Approach: Data Collection / Dataset: Use a small custom dataset of 100–200 samples per class, or leverage existing open-source ISL datasets. Model Development: Utilize MediaPipe Hands for real-time hand keypoint detection. Train a TensorFlow/Keras model (or PyTorch) using keypoints or preprocessed hand images. Model will classify characters, numbers, and basic words. Real-Time Inference: Implement webcam feed processing using OpenCV. Recognize gestures under 100ms. Deliverables: Trained model (.h5 or ONNX) Python inference script for live webcam recognition Sample dataset or instructions for collecting new samples README/documentation with setup and usage instructions Tools & Libraries: Python, TensorFlow / Keras, OpenCV Experience: Developed real-time hand gesture recognition systems using MediaPipe + ML models. Experienced with real-time video processing, deep learning deployment, and Python-based ML pipelines. I can provide a functional prototype quickly, demonstrating recognition of a few common gestures with real-time output. Best regards,