Emerged
Similar fonts
- BrandbeExtraBold
- Butter House Regular
- ckTrial Winner Sans UltraComp Extra Bold
- ckTrial Winner UltraComp Black
- ckTrial Winner UltraComp Extra Bold
- Darab Bold-medium
- DarabFaNum Bold-medium
- Data Trash
- Fit Devanagari Regular
- Fit Devanagari Variable Normal
- Fit Devanagari Variable Regular
- Fit Instances wdth109
- Fit Instances wdth110
- Fit Regular
- Fit Regular Regular Testing
- Fit Variable Normal
- Fit Variable Regular
- Flatbush Bold
- FSP DEMO - Robson Bold
- FSP DEMO - WnnrSnsltrCmpxBld Regular
- GC Magnu Bold
- Greater Regular
- Hunter Grunge
- Monbloc Heavy Ultra Condensed
- MultiType Pixel Compact
- Robson-Bold
- Schwachsinn Regular
- Sirkle Regular
- Sundown Regular
- Winner Sans UltraComp Black
- Winner Sans UltraComp Extra Bold
- Winner UltraComp Black
- Winner UltraComp Extra Bold
Details
How are we calculating this?
We analyze each font added to FontBase locally using the opentype.js library. We render the text off-canvas and measure key characteristics: stroke width; difference between bold and thin strokes; the relation of "x" letter height to capital letters; width of the letters compared to height; "i" size compared to "w" size. From these calculated numbers, we then build weight, proportion, xHeight and contrast. We also analyze the font's naming table to retrieve the basic info like font name, foundry and style names. No fonts are uploaded to our servers in the process.
How do we determine similar fonts?
Based on the characteristics we retrieve, on each font page, we select the fonts that have all the same parameter numbers. For example, if a font has 0.6 weight, we select fonts from our database that are in the range of 0.5-0.7 weight. We do this with all parameters combined, resulting in a similar font selection.