FX Wadag Medium Outline
Similar fonts
- 5b9cc6610be9aa77 - subset of Greta Mono Pro Thin Ita
- Berlingske Slab Condensed Thin Italic
- BerlingskeSlabCn-TnItalic
- Clan Offc Narrow Thin Italic
- Clan Offc Pro Narrow Thin Italic
- ClanOT-NarrowThinIta
- ClanOT-NarrThinItalic
- ClanPro-NarrowThinIta
- ClanPro-NarrThinItalic
- CS Gaspard Ascii Italic
- DBTOntheGo
- ETCAnybody-Thin
- FSP DEMO - Thicker Thin Regular
- FX Wadag ExtLt Ita Outline
- FZ JAZZY 14 3D ITALIC Normal
- Gavitrist-CondensedRegular
- Grenze 100italic
- Grenze Thin Italic
- GretaMonoPro-ThinItalic
- GretaSansStd-ThinIta
- IBM Plex Serif Thin Italic
- iCiel Zitrone FY
- Kicker Thin
- Mixolydian Thin Italic
- Monitor Condensed Trial Thin Italic
- Monitor Narrow Trial Thin Italic
- OksanaTextNarrowLight-Italic
- Pocas Trial Thin Condensed
- Quadraat Sans SC Offc Cond Thin
- SeagullOutline-Light
- Thicker Thin
- Zitrone FY Regular
- ZitroneFY
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.