FSP DEMO - Intro Head B UCG Regular
Similar fonts
- Bild v4 Thin
- Bild v5 Thin
- Forma Deck Narrow Hairline
- Forma Micro Narrow Hairline
- FSP DEMO - Gln Cndnsd Thn Regular
- FSP DEMO - Grt scp Nrrw l Regular
- Galeana Condensed Thin
- GreatEscapeNarrowUl-Regular
- Halvar Engschrift DEMO Hairline
- Halvar Engschrift Hairline
- Halvar Engschrift Hairline
- HalvarEngschriftTRY-Hairline
- HD Colton Comp Thin
- InterstateCondensed-Hairline
- InterstateT-HairlineCond
- Intro Head B UC G
- Kalkal Inliner Regular
- LHF Raven Regular Shadow
- LHF Raven Spurs Shadow
- Miura Narrow Pro Thin
- Miura Narrow Trial Thin
- Modica Pro Condensed Hairline
- Movida Condensed Hairline
- Movida Narrow Hairline Italic
- Superscience-HairlineCond
- TT Teds Hairline
- Typograf ExtraCondensed Thin Italic
- Typograf ExtraCondensed Variable_Trial Thin
- Typograf ExtraCondensed_Trial Thin
- Typograf ExtraCondensed_Trial Thin
- Typograf ExtraCondensed_Trial Thin Italic
- Typograf SuperVariable_Trial ExtraCondensed Thin
- Zed Round Semi-Condensed xsTRIAL Hairline Backslanted
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.