PROJECT 04 / SIGN LANGUAGE RECOGNITION
Teaching a computer to recognize hand signs.
This project explores how static hand signs can be cleaned, processed and matched to letters. The visual follows the journey from the original image to the final prediction.
STATIC HAND SIGN
STATIC LETTER SAMPLE
01 / ORIGINAL INPUT
02 / ISOLATED REGION
03 / PROCESSED MASK
04 / FEATURE RESPONSE
24 STATIC LETTER CLASSESPREDICTED LETTER /
LSTATIC LETTER OUTPUTThis visual was created for the portfolio to show the project idea clearly.
01 / CONTEXT
What we were trying to solve
Images of hand signs contain background, lighting and shape differences that can make recognition difficult. The project explores how preprocessing can isolate the hand before classification.
The scope stays with static images rather than real-time webcam recognition.
02 / PROJECT SCOPE
A focused processing study
The project follows 24 static letter classes from image input to letter output.
It is an image-processing and classification experiment, not a finished accessibility product.
03 / PIPELINE
How the image moves through the system
Each stage removes a little more visual noise before the final classification.
- 01Original static image
- 02Isolated hand region
- 03Processed mask
- 04Extracted or learned features
- 05Letter classification
- 06Output
04 / PROCESSING VIEW
The image changes before the model sees it.
The supporting views compare the same L-shaped hand geometry through preprocessing, then show the static model flow.
PREPROCESSING / SAME HAND POSITION
ASL LETTER L
STATIC-CLASS MODEL FLOW
24 STATIC LETTER CLASSES
- 01STATIC IMAGE→
- 02PREPROCESSING→
- 03FEATURES→
- 04CLASSIFICATION→
- 05LETTER OUTPUT
05 / REFLECTION
What I learned
The quality of the input can matter as much as the model that reads it.
- 01
Preprocessing makes the hand shape easier to separate from its background.
- 02
Comparing raw and processed images exposes where classification can become difficult.
- 03
A technical pipeline is easier to understand when every stage remains visually consistent.
WHERE IT STANDS NOW
A static hand-sign recognition experiment and processing study.
The portfolio visual keeps the scope at 24 static letter classes and does not attach a confidence claim.