RingsideCondensedOffice-Regular
Similar fonts
- AmsiProCond-Italic
- Calps-Light
- Datatype Condensed Black
- Datatype Condensed Regular
- Datatype Expanded Bold
- Datatype ExtraCondensed Light
- Datatype ExtraExpanded Bold
- Datatype ExtraExpanded Light
- Datatype SemiExpanded Regular
- Datatype SemiExpanded Thin
- ef83d0dc03ee3a70 - subset of Vita Cond Std Reg Ita
- Enterprise Sans Cond
- FS Industrie Cd Italic
- FSP DEMO - Light UltCond SeObli Italic
- GothamXNarrow-Book
- GT Standard L Narrow Light Oblique
- GT Standard Trial L Nr Lt Obl
- GT Standard Trial M Nr Lt
- GT Standard Trial M Nr Lt Obl
- Ioskeley Mono NL Light Semi-Condensed
- JetBrains Mono Semi Light
- Kurdis Test SemiCondensed
- Loos Compressed Light
- Netflix Sans Cd Light
- Oldschool Gtsk Cond Trial Bk It
- Pennsylvania Regular Italic SC
- Proxima Nova Extra Condensed Regular Italic
- Scotus Sans Condensed Book Italic
- Sofia Sans Condensed Italic
- Tourney Condensed Regular
- TTOctosquaresCond-XLight
- VerbCompressed-Italic
- Vin SlabPro-Italic
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.