Python package for RoboFont for calculating gray values.
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A calculation for the gray level of text

As any introduction to typography will tell you, font families come in different weights, ranging from thin to black and many steps in between. These weights are used to create hierarchies and structure within the text. A bold headline will catch the attention. A lighter weight for reading, etc. While the word weight has connotations of physics and empirical evidence, the typographic reality is a bit messy. Let's start by having a look at the data in the font.


While care is given to naming the different weights within a family, there are great differences between typefaces. There is no guarantee that the bold in one typeface has the same weight as a similarly named weight in another. This is usually not really a problem: typographers, designers and anyone selecting a font will do so by looking at the font first and not rely on the name. Bottom line is that the font name does not really provide reliable information about the weight.


The OpenType font format has the usWeightClass value [specification]. On a scale between 0 to 1000, this value can be used, for instance, to order the weights in a font in a menu, or to select a weight that is "bolder" than the current font. That looks more promising. Unfortunately, assigning the fontWeight value is not easy: older applications expect this value only in steps of 100 and some even respond with unexpected results to values under 250. There is not much room for families with many weights, the values have to be assigned "by hand" and often represent some sort of ranking and is seperate from any geometry in the font. So, like the font names, the fontWeight value can not really be used to make objective comparisons between typefaces.


But the weight differences are real: we can observe them by looking at text. So maybe we need to see how the individual marks on the page contribute to the weight of the text. And then work our way up. A glyph in a Latin font has a rectangle that is defined by its advance width and a height: either the font's units per em value, or the distance between ascender and descender. The shapes in the glyph may stick out of the rectangle, but generally they don't. In simple text shaping these rectangles are lined up next to each other. There are of course many, much more complex ways in which the glyphs can be arranged.

Assuming the shape is drawn with black on a white background and that the shape does not exceed the boundaries of the width or height, then the gray scale is going to be between black and white.

If there is no shape in the letter, the level is white. If the shape covers everything the result will be black. And then the steps in between. Here I need to remark that there are many shapes where this would not be true. For instance, a filled with a fine chess board pixel grid (one off, one on) would produce the same numerical result as a glyph that has a single black box on the bottom half. Optically these would not be the same at all.

All of the images above have a 50% coverage result. But (permitting a Gaussian blur to represent the diffraction in the eye) obviously b will appear as a much darker shape than d. So, if applied to a oddball fonts the results might not be very reliable. But I'm assuming we're looking at normal fonts and I'm permitting myself the luxury of not even defining what those are.

With all this in mind, the coverage of a single glyph can be expressed as:

coverage = area of the shape / (glyph advance width * units per em)


Suppose we calculate this gray level for each glyph in a font. Then what? A numerical average of all these glyph by glyph results will not produce a value that is useful. Each glyph will have a different coverage value, so we need to look at the distribution of the glyphs. How the font behaves in text. This introduces a new factor: language. We need to know how often certain shapes appear. The next question is then: which language? We need to make sure the tables are case sensitive: the frequencies of initial capitals in different languages are quite different from the lowercase frequencies. These values need to be normalized so that the sum of all frequencies is 1.

The gray level of a set of glyphs, in one language

coverage of one font in one language = sum of (gray level of a glyph) * (normalized glyph frequency)

Average of >1 languages

The numerical average of the densities calculated for a couple of languages can then be used as an relatively objective value for the color, or gray, of a specific font.

Supported in this code: Albanian, Basque, Bosnian, Catalan, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Norwegian, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish and Turkish.


We can also calculate a representative value for the average width of a font. There is no absolute minimum or maximum, but we can establish some rules for this value. Considering:

  • a numerical average width of all characters in a font will not be enough: we need to incorporate character frequencies for a couple of languages.
  • Font.unitsPerEm needs to be involved: we should be able to compare the width values of fonts with different ems. A TrueType version of the font with a 2048 em should get the same result as a CFF version with a 1000 unit em.


  • Single font, single language: sum (normalized freq * char width) / font.unitsPerEm

  • Avaerage of all languages: the numerical average of all results of all available languages

Comparing fonts with coverage and width

Here are two graphs of a couple of typefaces with different weights. Horizontally is the coverage value, plotted against Graph 1 Calculated font weight values plotted against the OS/2 weight class values.

Graph 1

  • Most "Regular" values are at 400, most "Bold" values at 700, but the corresponding coverage values are spread much wider. This illustrates that is not a reliable indicator for weight.
  • Letter Gothic Bold is actually lighter than most.
  • Times Bold and Caslon Bold have almost the same coverage.
  • Roboto Black has a weightClass value of 400, it looks out of place.
  • These typefaces were selected rather arbitrarily: I happen to have the data. The fonts might not be the latest version.

Graph 2 Calculated font weight values plotted against calculated font width values.

Graph 2

  • Letter Gothic has no width change from Medium to Bold.
  • Verdana gets quite a lot wider in the Bold.
  • Times Bold and Caslon Bold are very close in coverage and width.
  • Roboto has quite a range in weight without much change in width.


The frequency tables used in this calculation do not include punctuation or wordspaces. In many typefaces the punctuation symbols are lighter than the letters and that would skew the result. But then the texts used to compile the frequency tables would need to be examined as well: are all the texts on a comparable subject for instance? How do the average line lengths compare? Are there more or fewer quotations in the text? How does the word space fit in all this?

Such deep statistical analysis really is a different project. The languages are there to make sure there is no bias towards one specific language just by choosing one single frequency table.

  • The tables fairly represent the use of capitals and lowercase in a specific language.
  • Capitals and lowercase are the most interesting thing to measure in a font.
  • The tables used are all for languages that use the Latin script.
  • The font width is definitely a factor that might make the results less useful in extreme designs: wider typefaces have more weight in the glyph rectangle.
  • In order to be able to compare different typefaces it is necessary to calculate the densities with the same frequency tables. Which makes it difficult to compare across scripts.
  • I'm ignoring the effects kerning and tracking will have on the coverage. This will definitely be an interesting addition, it could factor in pair frequencies, but these would need to be correlated with the character frequencies.
  • I'm also ignoring any effects that might be caused by lighting conditions, inkspread, reading distance and type size, or any conditions of the eye.
  • Between the languages there are not only differences between the frequencies, but also between the charactersets. If we're analysing a font that is being worked on it might not have all the characters that a language needs. Rather then make assumptions about certain characters, we need to check first if a font has all the characters required by the table.


  • The code in this package is for RoboFont.
  • getFontCoverage(font) calculates the coverage for a single font, using frequency tables for 27 different languages.
  • getFontWidth(font) calculates a weighted average width for a font using the same languages.
  • calculateGlyphCoverage(glyph, font) calculates the coverage for a single glyph.
  • The module exports a frequency table and a function that filters the supported languages based on the glyphs in the font.


  • Pardon the basic interface. I don't want to complicate it with a UI right now.
  • Install the code.
  • In a script window:
from coverage import getFontCoverage

for f in AllFonts():
    print,, getFontCoverage(f), getFontWidth(f)


  • Calculate complete families, find fonts that have similar results, verify if these actually appear to have the same weight.
  • Perhaps weigh the shapes inside the xheight more than the shapes above or below.
  • Incorporate kerning pair frequencies for the languages in the table. Perhaps do the math for a single language first to get an idea of how much of a difference it would make.