A Neural-symbolic engine for understanding UI/UX context.
- JSON Tree analysis
- Text context detection
- Layout Group of interest clustering
- Button detection
- List detection
- Layout detection
- Graphic element detection
Method - CV Template matching (text removal postprocessing)
after removing the text, template matching could give us better accuracy.
Method - Tree Analysis (rule based)
With givven ui json tree, we analyze the position, alignment, the content of the button label's text to check if it is in Verb form.
Reflect Button Manipulation (Reverse Enginerring / ML)
With Reflect's Button system spec definition, we generate millions of possible button form, Train it, and use them for existing design's button detection.
Visibility Score (CV)
The score of specific element, which looks like pressable. Color Visibility, EM level, and more.
Context Score (NLP)
Simple NLP and with ui text content classification, we can score the content is likey to be a button text.
icon (vision based) classification and positioning on vector space indicating it's semantic space
Label your design with Bridged's Labeller
