EconSansCndThiIta
Similar fonts
- 203179a337b60044 - subset of Vita Std Thin Ita
- Amsi Pro Narrow Thin Italic
- BasicSansAltCnd-ThinIt
- BundaySlab-ThinIt
- Drive Mono Hairline Italic
- Drive Prop Hairline Italic
- Enhanced Dot Digital-7
- FAIRE Print Sans Trial Hairline
- Flink Neue Bauhaus Cmp Test Thin It
- Flink Neue Cmp Test Thin It
- Geogrotesque Thin Italic
- Geogrotesque-ThinIt
- Hapna Slab Serif Light
- Intro Head B G Shade
- Iosevka Thin Italic
- Italian Plate No1 Mono Ultralight Italic
- KievitOT-ThinItalic
- Lemon Sans Next Cond Thin Italic
- Margin MVB Extra Light Italic
- Martian B Thin
- MatahariCondensed-100CnThObl
- Nika Condensed_Trial ExtraLight
- Planer-ExtraLight
- Playpen Sans Thin
- PP Pangram Sans Rounded Condensed Thin Italic
- Sofia Sans Semi Cond Thin Italic
- Squad-ExtraLightItalic
- Taste-Thin
- Torcao-ConThiIt
- True Sans Mono Trial Thin
- TTSupermolotNeue-CondThinIt
- Urfa-ThinItalic
- We Benang Sans Variable Hairline
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.