Sign Language Recognition System
A gesture recognition system that translates hand signs into text and speech in real time from a webcam.
Project overview
The system tracks hand landmarks from a webcam feed, classifies the gesture against a trained sign vocabulary, assembles recognised signs into words and sentences, and speaks the result aloud through text-to-speech.
Landmark-based classification keeps it achievable without a GPU while still being a real deep learning project, and the accessibility framing gives the report a strong motivation chapter.
Problem statement
Communication between sign language users and people who do not sign depends on an interpreter being available. For everyday interactions — a shop, a clinic reception, a college office — no interpreter is present.
Proposed solution
Use a hand landmark detector to reduce each frame to a compact set of coordinates, classify those landmarks with a lightweight neural network, apply temporal smoothing so a held sign registers once, and convert the assembled text to speech.
Key features
- Real-time hand landmark tracking from a webcam
- Gesture classification over a trained sign vocabulary
- Temporal smoothing so one sign is not counted repeatedly
- Word and sentence assembly with a space and delete gesture
- Text-to-speech output for the assembled sentence
- Custom sign recording so users can extend the vocabulary
- Practice mode that shows the target sign and scores the attempt
Technology stack
Tracking
Classification
Speech
Interface
System architecture
The flow below is what goes into your architecture diagram and the corresponding chapter of the report.
- Capture reads webcam frames at a fixed rate
- Landmark stage extracts hand keypoints and normalises them for scale and position
- Buffer stage accumulates a short window of frames for dynamic signs
- Classifier predicts the sign from the landmark sequence
- Smoothing stage requires a stable prediction before accepting a sign
- Output stage appends to the sentence and speaks it on request
Expected output
- Live recognition with the predicted sign on screen
- Assembled sentence text and spoken audio
- Per-class accuracy across the trained vocabulary
- Confusion matrix identifying visually similar signs
Suitable branches, difficulty and duration
Documentation, review, demo and viva support
Documentation support
Abstract, problem statement, literature survey guidance, system architecture, ER and UML diagrams, module descriptions, test cases and screenshots — prepared in your department format.
Project review support
Delivery is split across your review calendar so review one has a design and plan, review two has working modules, and the final review has the integrated, tested system.
Demo support
A rehearsed demo path, seeded sample data, a reset script, and a recorded walkthrough kept as a backup in case something fails in the review room.
Viva preparation
A written question bank for this domain, a code walkthrough session, and practice at explaining each module in your own words — because the examiner will ask you.
Customisation options
Colleges reject duplicate topics, so most projects are adjusted before we start. These are the usual directions for this one.
- Train on Indian Sign Language instead of ASL alphabets
- Add two-handed sign support for a larger vocabulary
- Add a mobile version with on-device inference
- Add reverse translation that renders typed text as sign images
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Enquire about the Sign Language Recognition System project
Send your branch, college and first review date. You will get confirmation of scope, what the customisation would look like, and a delivery plan mapped to your reviews.