Typography
Typography based on context and purpose, over a singular stylistic vertical.
Role: Graphic Designer
Duration: 1 Month
Showcasing: Typography design
Context and Problem
Most typeface families are rooted in historic calligraphic or printing models, but these origins often detach the shapes and stylistic decisions from modern use cases. Traditional approaches solve this by offering multiple variants—such as display and body styles—but these mainly adjust parameters like x-height and tracking, rather than addressing deeper contextual or accessibility needs.
Approach
I approached this typeface with a user-experience perspective, focusing on accessibility and functional adaptation. Instead of offering static weights and styles, the goal was to design a system that morphs based on context—optimizing form for print, display, and body environments. This meant evaluating typographic dimensions such as stroke weight, aperture, and contrast not as aesthetic choices, but as functional responses to readability and the contextual medium.
Outcome
By identifying and simplifying typography’s core dimensions, I developed a typeface that adjusts its structure to improve legibility within specific contexts. This reduced the need for multiple, disconnected families and instead created a single system responsive to its environment.
Limitations
This font is not intended for branding or expressive typography. Its glyphs are optimized for legibility and clarity, which naturally limits stylistic range. The focus is on usability, not personality—built for accessibility rather than identity.
Beyond Text
Designing a typeface that morphs to its context became an exploration in how accessibility and adaptability can shape form. The final system used a fixed set of glyphs optimized for print, display, and body, but the broader concept imagined typography that could evolve dynamically — even altering phrasing to suit each context or situation. While that vision reached beyond current technical limits, it set the foundation for context-aware design. My final typography design is the result of prioritizes usability over style, adapting to users instead of expecting them to adapt to it.
System Thinking
I began thinking about change at a systemic level, breaking typography into layers of influence. Layer 1, at the atomic scale, explores how a glyph could physically change form based on need — for example, adapting forms to improve character recognition for dyslexic readers. Layer 2 examines how glyphs interact as neighbors within words and sentences, including adjustments to tracking for different purposes like paragraph versus title text, and how those typographic levels relate to one another. Layer 3 considers the medium — print, digital, or environmental signage — and how each medium affects form and legibility. Finally, Layer 4 looks at situational context: reading directions on a laptop, glancing at a phone while driving, or checking a smartwatch mid-run. Each layer introduces new complexity and corresponding design challenges.
Different Dimensions
I used a mind map to explore the many factors that influence how text changes, then narrowed my focus to accessibility, information hierarchy, and material. This deliberate scope kept the project aligned with my graphic design background while avoiding unnecessary complexity. Rather than over-designing a solution, I chose to refine a few key dimensions deeply — ensuring that each decision stayed meaningful, feasible, and grounded in the user experience.
Out-of-scope examples included contrast adjustments that respond to environmental brightness. While this could be addressed through features like light and dark modes, color changes, or opacity shifts, those decisions fall within the responsibility of the interface designer rather than the type designer. Cultural and linguistic adaptation was another potential dimension—expanding beyond English or tailoring phrasing to cultural context—but this introduced significant complexity. Creating glyphs for multiple languages and designing for culturally specific meaning or sentiment would exceed the project’s scope. For these reasons, Layer 4: situational context was intentionally excluded from this exploration.
Research
I conducted research to better understand how each typographic parameter affects readability and accessibility. For example, individuals with low vision struggle to distinguish fine details, requiring adjustments such as a larger x-height, open counters, wider letter spacing, and thicker strokes. I compiled these findings into a comparative table to identify overlapping solutions and visualize how multiple accessibility challenges could be addressed through shared design decisions.
Weighing Design Decisions
While I could have created a separate font for each parameter, the result would have been overly complex and less effective—simplicity is key. Additionally, many parameters can conflict with one another. For example, thinner strokes may improve legibility by creating clearer shapes, but they also reduce contrast and visual weight. Designing a single, adaptable system required balancing these opposing forces rather than isolating them into individual solutions.
Converging
I was able to find the changes that could be applied to all dimensions without reducing the design quality in another dimension. These common denominators became my fixed attributes.
Fixed Attributes:
Grounded Bottoms, No Serifs, Distinct Shapes, Open Counters
Then, identifying the variables that needed to be changed depending on the context or situation. These variable attributes were calling the design shots.
Variable Attributes:
Paper, Ink Traps, Large Text, Lower or average x-height, Thicker strokes, Standard letter spacing, Small Text, Higher x-height, Thinner strokes, Wider letter spacing
I translated these findings into three distinct fonts designed to adapt to different contexts: Print, Display, and Body. Instead of following traditional weight conventions like Regular or Bold, I named each style based on its semantic purpose. This shift emphasized that the variations weren’t purely aesthetic but functional, with multiple parameters—such as weight, spacing, and form—adjusting intentionally to enhance readability within each specific context.
Creating a Baseline
To create meaningful contextual adjustments, I first needed a neutral baseline. Using an existing open-source font wasn’t ideal since it would represent only a single point on the spectrum—and most carry distinct stylistic biases. Instead, I set out to design the most average font possible by aligning and averaging the glyphs from six typefaces—Helvetica, Arial, Verdana, Futura, Roboto, and Inter—to establish a balanced, style-free foundation.
Creating an accurate average shape required several iterations. Hand-drawing based on perception proved too imprecise (1), and manually tagging key points—like annotating X-rays—was too slow (2). Eventually, I overlaid all six fonts at 33% opacity and used AI to reconstruct the areas that appeared as pure black (3). This reductive method worked, allowing me to trace the composite letterforms and export precise SVGs for font generation.
To keep costs low, I built the baseline font in FontForge rather than the industry-standard Glyphs. I named it Combo, reflecting its composite origin. Beyond serving as a foundation, the process became a learning exercise in fine-tuning typography—adjusting line height through winAscent and winDescent, configuring kerning lookups, contextual alternates, and refining optical positioning for balanced spacing.
Initial Learnings
Using the baseline font as a foundation, I explored the nuances of building a complete font family — refining line height (1), kerning lookups (2), and optical positioning (3) to create balance and readability. I learned how to increase the line height in FontForge to around 120% of the point size. This improved vertical rhythm and legibility instead of having point size and line height the exact same. I found lookups as a way to define kerning classes for common character forms (like “O, ” “Q,” and “C”). Through optical adjustments, I corrected visual imbalances by allowing curved shapes to overshoot guides for more natural alignment. I chose not to implement contextual alternates (4), as the complexity outweighed the benefit for this project. Together, these refinements transformed the baseline into a cohesive, readable, and visually consistent type system.
Creating the Font
The baseline provided the core shapes for my typeface. Building from it, I incorporated the fixed attributes and developed three distinct styles to handle variable contextual adjustments.
Grounded Bottoms
Using the baseline font, I applied the fixed attributes identified earlier. While Combo was already a sans serif, it required refinements for grounded bottoms, distinct shapes, and open counters.
Grounded Bottoms: Glyphs were adjusted with thicker, wider bases to counteract the visual lightness that occurs along the baseline, where white space tends to accumulate. Letters such as n, e, a, and o were refined to reduce tapering and create a more stable visual foundation.
Distinct Shapes
Ambiguous characters were redesigned for clarity. The q gained a longer tail to distinguish it from p, and the 0 received a diagonal slash to differentiate it from O. Each character’s form was also given subtle left–right variations so that letters like b and d read as unique shapes rather than mirrored copies. This was achieved by thickening the bowl-to-stem connection on one side and inverting it on the other to create directional contrast.
Open Counters
Line endings were shortened to create more open interior spaces, preventing visual closure that could make similar letters harder to distinguish.
Variable Adjustments
I made a series of fine, detailed adjustments across the different font styles to ensure each one performs optimally in its intended context. The table below outlines the distinctions between the body, display, and print fonts, informed by the research conducted. Because body and print fonts prioritize readability, their baseline strokes are slightly thicker than those of the display font. The same principle applies to open counters, which are widened to maintain clarity at smaller sizes. Tracking is also increased for body and print fonts so that individual characters remain easily distinguishable in dense text. Additionally, the x-height is raised in these styles to enhance legibility, while the display font features a slightly higher stroke contrast to create visual hierarchy and emphasis in larger settings. Finally, ink traps were incorporated into the print font to prevent blurring and maintain sharpness when printed at smaller scales.
Adjustment
Baseline Size
Open Counters
Tracking (width)
X-Height
Stroke Thickness
Ink Traps
Combo (Baseline)
0% thicker
34% open
50
480
Average: 108px
No
Morph (Body)
10% thicker
56% open
70
480
25.9% thinner
No
Morph (Display)
5% thicker
37% open
50
471
3.7% thicker
No
Morph (Print)
10% thicker
56% open
70
480
25.9% thinner
Yes
Vector Adjustments
Once the foundational shapes were established, I refined each glyph through precise vector adjustments to improve balance and visual flow. Subtle shifts to anchor points and Bézier curves helped smooth contours, correct weight distribution, and enhance consistency across related forms. I focused on aligning vertical and horizontal stress, maintaining even stroke contrast, and minimizing unnecessary points to ensure cleaner outlines and smoother scaling. These micro-adjustments not only improved optical harmony within individual letters but also created a more cohesive rhythm when viewed in text, reinforcing both legibility and aesthetic precision.
Font Characters
Display
Body
Using the Font
A collection of examples showing the font in action — highlighting how its form adapts to different design contexts and intended purposes.
Testing the Font
To evaluate Morph’s performance, I asked GPT-5 to compared it against six widely used sans-serif typefaces—Helvetica, Arial, Futura, Roboto, Verdana, and Inter (the same ones used to create the baseline)—across three contexts: Display, Body, and Print. Each category was assessed using consistent criteria: legibility, readability, visual balance, stroke contrast, and spacing efficiency.
In Display tests (large titles and visual hierarchy), Morph – Display ranked second overall, showing strong edge definition and balanced contrast while maintaining clarity at smaller display sizes. In Body text evaluations (digital screens and extended reading), Morph – Body tied closely with Inter, performing especially well in sustained readability thanks to its slightly thicker baseline and open counters. In Print testing, Morph – Print ranked first, with its refined stroke weight and subtle ink traps significantly reducing bleed and improving sharpness at small sizes.
Overall, Morph proved highly adaptable—its context-specific adjustments consistently improved clarity and comfort, matching or exceeding industry-standard fonts across multiple reading environments.
Final Thoughts
Creating this typeface was an opportunity to deepen my understanding of how subtle design decisions influence readability, tone, and personality. From the earliest sketches to the final digital refinements, each step revealed how structure and form work together to create visual harmony across different contexts.
Through this process, I learned to balance precision with expression — maintaining consistent metrics while allowing room for character and flexibility. Experimenting with optical adjustments, contextual alternates, and use-case variations helped me design a system that adapts fluidly to print, display, and body text needs.
Ultimately, this project reaffirmed that typography is more than letterforms — it’s a visual language that shapes how people read, feel, and connect with content.
My perfectionism tells me this font is far from perfect, but the strides made were momentous. The font is not publicly available, but feel free to reach out if you are interested in trying it out.