H2V (hand to voice0

Inspiration

I was inspired by the struggles of people who can’t speak but communicate through gestures. I wanted to give them a voice, something that turns their silent language into speech, so they feel heard and included. After building a hand tracker, I realized I could use it for something more meaningful. This project is my way of using technology not just to create, but to care.

What it does

Through real-time translation of hand gestures into spoken language, this project provides a voice to those who are mute. It recognizes particular gestures and tracks hand landmarks using a webcam and MediaPipe. For example, "Hello" is represented by an open palm, "Goodbye" by a peace sign (index and middle fingers raised), and "Yes" by a thumbs-up. Following detection, each gesture is translated into spoken English using pyttsx3, and then Google Text-to-Speech (gTTS) is used to translate it into Hindi. This bridges the gap between speech and silence by allowing nonverbal users to communicate with those around them in a clear and immediate manner.

How we built it

We used Python to build this project, along with essential libraries like MediaPipe for precise hand tracking and landmark detection, and OpenCV for real-time webcam video capture. In order to identify 21 hand landmarks, we first set up a functional webcam feed and integrated MediaPipe. Then, using finger positions, we defined logic to identify particular gestures: a thumbs-up for "Yes," a peace sign for "Goodbye," and an open palm for "Hello." When a gesture is identified, a speech output is produced using gTTS (Google Text-to-Speech) for Hindi and pyttsx3 for English. Both comprehension and local translation are aided by this dual-voice output. We also dealt with issues like controlling camera responsiveness and avoiding repetitive outputs. Because the entire system operates in real time, non-verbal users can communicate easily and hands-free.

Challenges we ran into

Some of the key challenges we faced included gesture misclassification and voice lag. At first, the program would speak even before it detected any gestures because the speech function wasn’t connected properly to gesture recognition. Another problem was that once a gesture was detected, the camera would freeze while the text-to-speech engine was running, interrupting the smooth, real-time experience. Installing certain libraries like mediapipe, pyttsx3, and playsound also created difficulties due to compatibility and Python version issues. We had to manage package versions carefully and switch interpreters to make sure everything worked together. Improving gesture detection accuracy and preventing repeated outputs were also significant hurdles we overcame.

Accomplishments that we're proud of

Our model can recognize simple hand signs like an open palm, a peace sign, and a thumbs-up using MediaPipe. It then translates these signs into spoken words like “Hello,” “Goodbye,” and “Yes.” We combined real-time hand tracking with voice output to enable instant gesture-to-speech translation. Although we faced challenges with library installations, hardware limitations, and gesture misclassification, we worked through these problems to provide a smooth and intuitive experience. We are especially proud of how accessible and empowering this tool can be for non-verbal individuals, giving them a simple yet effective way to communicate.

What we learned

We learned how to combine computer vision and speech synthesis to address a real-world accessibility issue. Through this project, we improved our understanding of hand landmark detection with MediaPipe. We managed real-time webcam input using OpenCV. We also converted recognized gestures into spoken words with tools like pyttsx3 and gTTS. We learned how to fix Python environment problems, manage library dependencies, and improve code for real-time performance. Most importantly, we discovered how technology can be used in a meaningful way to empower people and give a voice to those who need it most.

What's next for H2V (hand to voice)

Next, we plan to expand H2V’s abilities by increasing the number of recognizable gestures. This will support a wider range of sign language, including dynamic, motion-based signs. We aim to introduce sentence formation using gesture sequences. We also want to integrate multilingual speech output so users can communicate in different languages. Additionally, we hope to create a mobile app version for better accessibility and portability. In the future, we also plan to add facial expression detection to capture emotional tone and make interactions feel more natural. Our ultimate goal is to develop H2V into a smart and inclusive communication tool that truly empowers the non-verbal community.

Built With

• along-with-the-**mediapipe**-library-for-real-time-hand-tracking-and-gesture-recognition.-for-speech-synthesis
• and-**playsound**-to-play-the-generated-speech.-the-interface-is-built-using-**opencv**
• apis
• beyond
• cloud-services
• i
• or
• python
• used
• we-integrated-**gtts-(google-text-to-speech)**-to-convert-text-into-audio
• which-captures-webcam-input-and-displays-the-live-video-feed-with-detected-hand-landmarks.-this-project-was-developed-and-tested-on-the-**windows-platform**-using-local-resources
• without-relying-on-external-databases