Zenga-Light
Similar fonts
- AddressSansPro-ExtraLightIt
- CommaBase-HairlineItalic
- Dean Gothic Condensed ExtraLight Italic
- Dezert Outline Dash
- Forma DJR Banner Nar Lt It
- Forma DJR Dsp Nar Lt It
- Forma DJR Txt Nar Lt It
- FOTAmena-CondensedRegular
- FOTSolaneOutline
- FSP DEMO - Sz Cndnsd Lght Italic
- Hawkes Light Regular
- IBM Plex Sans Condensed ExtraLight Italic
- Lateral TRIAL Condensed Thin Italic
- LisboaExtraLight-Italic
- Matria Test ExtraLight Italic
- MNKY Jane CY Condensed Thin Italic
- MoMA Sans Cond Web Light Regular
- Nauman Neue Test Condensed Light Italic
- Nauman Neue Trial Condensed Light Italic
- Noto Rashi Hebrew ExtraLight
- Noto Serif Hentaigana ExtraLight
- Noto Serif Hentaigana Regular Variable
- RF Dewi Condensed Ultralight Italic
- Roboto Serif 20pt UltraCondensed Thin
- Roboto Serif 28pt ExtraCondensed Thin
- Roboto Serif 28pt ExtraCondensed Thin Italic
- Semana Sans ExtraLight Italic
- SpiegelSansCd Light Italic
- TisaPro-ThinIta
- Ufes Sans Thin Italic
- Unicod-Cond Condensed 6
- VVDS Fifties Medium Thin Italic
- Zenga-Light
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.