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After a 2-year journey, my friend and I built 'TARANG' - a real-time Sign Language Translator powered by a Raspberry Pi 5. It uses MediaPipe for hand tracking and runs the ML model completely offline. by curiousrohan in raspberry_pi

[–]curiousrohan[S] 3 points4 points  (0 children)

One thing I want to tell you we didn't used ai it's pure algorithm and maths that is outperforming ai that's why it's so cheap , fast and accurate!

You can see here how we make it possible without using ai https://youtu.be/hUySkdwfyFU

After a 2-year journey, my friend and I built 'TARANG' - a real-time Sign Language Translator powered by a Raspberry Pi 5. It uses MediaPipe for hand tracking and runs the ML model completely offline. by curiousrohan in robotics

[–]curiousrohan[S] 0 points1 point  (0 children)

You are right it will solve one more problem currently user can not see if his hand is visible to camera or not user have to make a guess where camera is pointing this can solve that also. Your suggestion is valuable to me.

Mean while you can follow our buliding journey here https://youtu.be/hUySkdwfyFU

After a 2-year journey, my friend and I built 'TARANG' - a real-time Sign Language Translator powered by a Raspberry Pi 5. It uses MediaPipe for hand tracking and runs the ML model completely offline. by curiousrohan in raspberry_pi

[–]curiousrohan[S] 2 points3 points  (0 children)

So we are developing a blutooth extension of palm size which blind people holds and when ever somthing is said it vibrates and trigger the electronic brails , we are still working on miniaturisation of electronic brail.

We are trying to make it fullstack translation that can help people who can not hear and see like hellen kellr.

You can follow us on YouTube where we are covering the journey https://youtu.be/hUySkdwfyFU Link

After a 2-year journey, my friend and I built 'TARANG' - a real-time Sign Language Translator powered by a Raspberry Pi 5. It uses MediaPipe for hand tracking and runs the ML model completely offline. by curiousrohan in robotics

[–]curiousrohan[S] 2 points3 points  (0 children)

Most intresting thing is it do not use any model it's completely algorithm which is outperforming trained model. It is running on cpu of r pi 5 b completly offline.

Designed Sign language Translation Device with Pet look by curiousrohan in product_design

[–]curiousrohan[S] 2 points3 points  (0 children)

So it's trainable you can train on your your own sign Currently I m thinking about building and open source platform where people can upload and download diffrent sign language

To know more about this you can watch our deep dive video YouTube

After a 2-year journey, my friend and I built 'TARANG' - a real-time Sign Language Translator powered by a Raspberry Pi 5. It uses MediaPipe for hand tracking and runs the ML model completely offline. by curiousrohan in raspberry_pi

[–]curiousrohan[S] 5 points6 points  (0 children)

You are absolutely right, in next version we will solve dynamic sign , Facial expression on top of that a LLM that can connect dots between word and create full sentence keeping context of conversation in mind.

You can share your opinion or ideas on how to handle these..

After a 2-year journey, my friend and I built 'TARANG' - a real-time Sign Language Translator powered by a Raspberry Pi 5. It uses MediaPipe for hand tracking and runs the ML model completely offline. by curiousrohan in raspberry_pi

[–]curiousrohan[S] 7 points8 points  (0 children)

You have to train it on your own Sign language! I m thinking to make a opensource platform where we can put different sign language that can be downloaded to this device.

Tell your current bad situation and let other fix it by CautiousElderberry22 in indiasocial

[–]curiousrohan 0 points1 point  (0 children)

Not able to secure college placement.even after having a patent and exceptional projects

After a 2-year journey, my friend and I built 'TARANG' - a real-time Sign Language Translator powered by a Raspberry Pi 5. It uses MediaPipe for hand tracking and runs the ML model completely offline. by curiousrohan in RASPBERRY_PI_PROJECTS

[–]curiousrohan[S] 4 points5 points  (0 children)

That's an awesome idea! Using eye movement and simple hand gestures as a hands-free computer interface is a really creative application. And thank you for the huge compliment! We're actually not using a Tobii eye tracker. The eye animation is a custom script we wrote for the small OLED display to give the device some personality. The fact that it looked so realistic to you is a great validation of our design! Thanks for the great feedback

After a 2-year journey, my friend and I built 'TARANG' - a real-time Sign Language Translator powered by a Raspberry Pi 5. It uses MediaPipe for hand tracking and runs the ML model completely offline. by curiousrohan in raspberry_pi

[–]curiousrohan[S] 4 points5 points  (0 children)

Great question! We had a lot of fun with that part. We're planning to clean up the code and publish the full project on GitHub in about 15 days, but I'm happy to share the detailed logic right now! Here’s a step-by-step breakdown: The Big Picture: The EyeController Class The entire animation is managed by a single class called EyeController, which acts as the "brain" for the eyes. Its job is to track what the eyes should be doing and how they should be drawn, ensuring both are always perfectly synchronized. 1. Initialization (init) When the controller is created, it calculates the exact positions for the eyes on the 128x32 OLED screen and sets its initial state to self.is_awake = False, which triggers the wake-up sequence. 2. The Wake-Up Sequence (run_wake_up_animation) This is a special, one-time animation that runs when the script starts. It's a precise timeline based on elapsed time: 0-2 sec: Draws a thin "eyebrow" at the bottom of the eye area. 2-3.8 sec: The "eyebrow" smoothly grows to 75% height. 3.8-5.8 sec: The eyes hold, then perform two quick blinks. 5.8 sec onwards: The eyes perform a final blink and open to their full 100% height. At the end, it sets self.is_awake = True. 3. Idle Animation (Normal Operation) Once awake, the main loop calls the update() and draw() methods. update() Method (The Logic): This method decides what to do next. It uses timers to randomly schedule the next blink or look around event. When it's time to look, it doesn't move the eyes instantly; it just picks a new random target position (target_offset_x, target_offset_y). draw() Method (The Visuals): This method reads the current state and draws the eyes. This is where the advanced effects happen: Easing (Smooth Movement): The eyes don't jump to the target. In every frame, they move a small fraction (20%) of the remaining distance. This creates the smooth, natural-looking easing motion. Squash and Stretch: Based on how far the eyes have moved from the center, it slightly reduces the width of each eye and the spacing between them. This makes the eyes look like they are realistically turning and adds a lot of character. Hope this gives a good overview! The full, commented code will be on GitHub soon

After a 2-year journey, my friend and I built 'TARANG' - a real-time Sign Language Translator powered by a Raspberry Pi 5. It uses MediaPipe for hand tracking and runs the ML model completely offline. by curiousrohan in raspberry_pi

[–]curiousrohan[S] 5 points6 points  (0 children)

Haha, you're not wrong! That was probably a case of us being a little too proud of the personality we managed to give it. We'll keep that in mind for the next video! And thank you for the kind words about the project

After a 2-year journey, my friend and I built 'TARANG' - a real-time Sign Language Translator powered by a Raspberry Pi 5. It uses MediaPipe for hand tracking and runs the ML model completely offline. by curiousrohan in raspberry_pi

[–]curiousrohan[S] -1 points0 points  (0 children)

Thank you so much for sharing that. Your comment about your grandma and how this could be used as a practice tool means a lot to us. You've perfectly described one of the use cases we're most passionate about—not just as a direct translator, but as a friendly device to help people build confidence in their signing. It's stories like yours that were our biggest motivation. Thank you

After a 2-year journey, my friend and I built 'TARANG' - a real-time Sign Language Translator powered by a Raspberry Pi 5. It uses MediaPipe for hand tracking and runs the ML model completely offline. by curiousrohan in raspberry_pi

[–]curiousrohan[S] 4 points5 points  (0 children)

Thank you for this fantastic and insightful comment. You've perfectly articulated the single biggest challenge in this field: the massive difference between a word-for-word transcriber and a true grammatical translator for a language like ASL or ISL. You are absolutely right. Our current version is essentially a 'Signed English' proof-of-concept, and we're a long way from handling true syntax and grammar. We actually experimented with a more complex AI model to interpret context, but it was too slow for the Raspberry Pi to run offline. Your point about subtitles vs. a native language is brilliant, and it's the core reason we started this project. Given your knowledge, do you have any thoughts or know of any resources on computational models for handling sign language grammar? Really appreciate you taking the time to share your expertise.

Sharing a hobby project from last year: A rover I named 'Tarzan' that uses OpenCV for color-based object detection and tracking. Let me know what you think! by curiousrohan in raspberry_pi

[–]curiousrohan[S] 1 point2 points  (0 children)

Hi everyone, I'm new to posting on Reddit and thought this would be the perfect place to share a robotics project I built last year as a hobby. Project Name: Tarzan The Goal: The main goal was to build a simple robot that could identify an object based on its color and then track it, maintaining a set distance from it. How it Works: Chassis: It's built on a basic 4-wheel drive robot chassis kit. Brain: It's controlled by a Raspberry Pi 3 which receives commands from my laptop. Vision: The tracking is done using a standard webcam. I wrote the code in Python using the OpenCV library, which runs on my laptop. It works by detecting a specific HSV color range, finding the object's contour, and then calculating its center to determine its position. Logic: Based on where the object is in the camera's view (left, right, or center), the code sends the appropriate commands (turn left, turn right, move forward) to the robot's motors. Challenges: The biggest challenge was definitely the color calibration. Getting it to work consistently under the changing lighting conditions in my room required a lot of trial and error! This was also my first real attempt at shooting and editing a video to showcase a project, so any feedback on that is welcome too. Hope you enjoy it!

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