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Abstract:
This video humorously explores the concept of extending typography beyond standard uppercase and lowercase, proposing "uppestcase" and "lowestcase" letters. Initially attempting manual creation, the presenter pivots to a machine learning ("derp learning") approach. Using a large dataset of fonts, cleaned via custom tools (including automatic detection of identical upper/lowercase forms), several models are trained. Early vector-based models produce "derpy" results like the "Comic Sands" font. Improvements using Signed Distance Fields (SDFs) for representation and a novel dual-model training system (where lowercase and uppercase models generate training data for each other via a feedback loop) yield more coherent, though still stylized, fonts like "Futurda," "Hevenica," and "Exponential New Roman." The models are explored interactively and used for tangential experiments like optimizing letter shapes ("Perfect Hallucination" font) and creating unique chess-playing AIs. Iteratively applying the models reveals a "lowestcase" limit (converging to dots) and an "uppestcase" limit (a stable, looping 3D shape), which is ultimately 3D printed. The video concludes by humorously suggesting the lowestcase form is the colon (:) and the uppestcase form could replace the Caps Lock key.
From Lowercase to Lowestcase: A Machine Learning Journey into Extreme Typography
0:00 Introduction to Case Concepts: The video starts by discussing the conventional uses of uppercase (anger, yelling, proper nouns, German nouns) and lowercase (casualness) and poses the need for levels beyond these.
1:12 The Need for "Uppestcase" and "Lowestcase": The core idea is introduced: creating letter forms that are more uppercase than uppercase and more lowercase than lowercase.
1:35 Manual Attempt & Font Availability: A quick manual drawing of an extended 52-character alphabet demonstrates the concept's arbitrariness. All fonts created in the video are offered for download.
2:14 Embracing Over-Complication with "Derp Learning": The presenter opts for an automated machine learning approach instead of the simple manual one, acknowledging the likely complexity and imperfect results ("derp learning").
2:37 Machine Learning Approach: The plan is to train a model using font examples (uppercase input, lowercase output) to learn the transformation, enabling it to lowercase any shape, including already lowercase letters.
3:22 Data Gathering and Representation: Fonts are used as the data source, initially using their native vector representation (lines and Bezier curves).
3:57 Font Curation Challenges: Downloading numerous fonts introduces problems: many are unsuitable (dingbats like Star Wars logos or dog drawings, broken fonts) or lack distinct upper/lowercase forms. A custom tool is built for filtering.
4:54 Automatically Detecting Identical Cases: A program using black box optimization is developed to automatically identify and exclude fonts where the uppercase and lowercase letters are merely scaled/shifted versions of each other.
5:45 First ML Model Attempt (Vector-based): Using custom ML software, a model is trained on vector representations. The results are poor ("derpy"), leading to the creation of the "Comic Sands" font from Comic Sans.
8:05 Improved ML Model (Better Error Metric): Refining the model's learning process by allowing it to match shapes regardless of point order leads to better, more recognizable results. This model is used to create the "Futurda" font from Futura.
10:30 Second ML Approach (Pixel-based with SDFs): A new approach uses pixel grids represented by Signed Distance Fields (SDFs) at 36x36 resolution, a more compact and ML-friendly format.
12:07 Novel Dual-Model Training: To handle the lack of "lower-er case" examples, two models (make lowercase, make uppercase) are trained simultaneously. They generate training data for each other in a feedback loop, enforcing the idea that they should be opposites.
13:54 Training and Results (SDF/Dual Model): After extensive training (~1 month), this approach yields fonts like "Hevenica" & "Special Helvetica" (from Helvetica) and "Exponential New Roman" & "Plus New Roman" (from Times New Roman).
15:04 Interactive Model Exploration: A custom UI demonstrates the models transforming hand-drawn shapes in real-time and predicting the letter identity (e.g., distinguishing O from Q).
18:44 Side Quest 1: Optimal Letter Hallucinations: The model's letter prediction capability is used to generate and optimize random shapes to find what the model considers the "perfect" representation of each letter, resulting in the "Perfect Hallucination" font.
20:27 Side Quest 2: Letter-Based Chess AI: The letter recognition model is used to create 26 chess algorithms, where each letter plays moves that make the board visually resemble itself. They perform poorly.
21:40 Discovering the Limits: Lowestcase and Uppestcase: Iteratively applying the "make lowercase" model causes letters to shrink into dust ("lowestcase," resembling two dots). Iteratively applying the "make uppercase" model leads to a stable, repeating loop ("uppestcase").
22:26 3D Printing the Uppestcase Letter: The frames of the "uppestcase" loop are stacked to create a 3D model, which is then successfully 3D printed (after some breakage and repair).
23:36 Final Thoughts: The "lowestcase" form is identified as the colon (:), already on the keyboard, and the 3D-printed "uppestcase" object is jokingly proposed as a replacement for the Caps Lock key.