The Cognitive Type Project is focused on developing computational tools to enable the design of typefaces with varying cognitive properties. This initiative aims to empower typographers to craft fonts that enhance click-through rates for online ads, improve reading levels in children's books, enable dyslexics to create personalized type, or provide insights into customer reactions to textual content in media. A significant challenge in research related to mapping typography to cognition is the creation of thousands of typefaces with minor variations, a process that is both labor-intensive and requires the expertise of skilled typographers. Cognitive science research highlights that the design and form of letters, along with the text's overall layout, are crucial in determining the ease of reading and other cognitive properties of type such as perceived beauty and memorability. These factors affect not only the legibility and clarity of information presentation but also the likability of a typeface.

This work presents an accuracy study of the open source OCR engine, Kraken, on the leading Arabic scholarly journal, al-Abhath. In contrast with other commercially available OCR engines, Kraken is shown to be capable of producing highly accurate Arabic-script OCR. The study also assesses the relative accuracy of typeface-specific and generalized models on the al-Abhath data and provides a microanalysis of the ``error instances'' and the contextual features that may have contributed to OCR misrecognition. Building on this analysis, the paper argues that Arabic-script OCR can be significantly improved through (1) a more systematic approach to training data production, and (2) the development of key technological components, especially multi-language models and improved line segmentation and layout analysis. Cet article pr{é}sente une {é}tude d'exactitude du moteur ROC open source, Krakan, sur la revue acad{é}mique arabe de premier rang, al-Abhath. Contrairement {à} d'autres moteurs ROC disponibles sur le march{é}, Kraken se r{é}v{è}le {ê}tre capable de produire de la ROC extr{ê}mement exacte de l'{é}criture arabe. L'{é}tude {é}value aussi l'exactitude relative des mod{è}les sp{é}cifiquement configur{é}s {à} des polices et celle des mod{è}les g{é}n{é}ralis{é}s sur les donn{é}es d'al-Abhath et fournit une microanalyse des "occurrences d'erreurs", ainsi qu'une microanalyse des {é}l{é}ments contextuels qui pourraient avoir contribu{é} {à} la m{é}reconnaissance ROC. S'appuyant sur cette analyse, cet article fait valoir que la ROC de l'{é}criture arabe peut {ê}tre consid{é}rablement am{é}lior{é}e gr{â}ce {à} (1) une approche plus syst{é}matique d'entra{î}nement de la production de donn{é}es et (2) gr{â}ce au d{é}veloppement de composants technologiques fondamentaux, notammentl'am{é}lioration des mod{è}les multilingues, de la segmentation de ligne et de l'analyse de la mise en page.

Computational Design approaches facilitate the generation of typographic design, but evaluating these designs remains a challenging task. In this paper, we propose a set of heuristic metrics for typographic design evaluation, focusing on their legibility, which assesses the text visibility, aesthetics, which evaluates the visual quality of the design, and semantic features, which estimate how effectively the design conveys the content semantics. We experiment with a constrained evolutionary approach for generating typographic posters, incorporating the proposed evaluation metrics with varied setups, and treating the legibility metrics as constraints. We also integrate emotion recognition to identify text semantics automatically and analyse the performance of the approach and the visual characteristics outputs.

One notable application is the semantic typographic logo, which symbolizes a unique identity in a concise yet informative manner. Due to its expressiveness and memorability [7], semantic typographic logo has been widely used as visual signatures for individuals [28], brand logos with commercial values [15, 20], and symbols for significant events and city promotions [3, 43]. However, crafting a semantic typographic logo presents a formidable challenge, requiring seamless blending of typeface and imagery while preserving readability. Experienced designers often rely on professional software like Adobe Illustrator to manually adjust the outline of the typeface to incorporate specific imagery, which is a time-consuming and error-prone process. They often experiment with different strokes or letters of typeface and various imageries to find a visually appealing and memorable representation, intensifying the lengthy process. This requires creative thinking, practical skills, and the ability to persist through continuous trial and error.

Forbes recently shared an article about the $65 million funding received by Typeface, a generative AI application for enterprise content creation. The startup lets companies upload their existing content such as web pages, blogs, Instagram posts, brand logos and other visual assets (a brand's personalized data set according to the company) and combines it with public data to train Typeface's AI model to generate future content. On the surface, for people who don't have a deep understanding of brand, this likely seems amazing. But with the above brand inputs only, there are HUGE gaps in the creation of MEANINGFUL content. Let's explore some of the myths on which this and other similar types of AI are based that are sending us in the wrong direction. Have you heard the phrase, "bad inputs bad outputs"?

A scalar is just a single number, in contrast to most of the other objects like Vectors, which are usually arrays of multiple numbers. We write scalars in italics. We usually give scalars lower-case variable names. When we introduce them, we specify what kind of number they are. "Let n N be the number of units," while defining a natural number scalar.

Remember all those classics you devoured in comp-lit class? Research shows that we retain an embarrassingly small sliver of what we read. In an effort to help college students boost that percentage, a team made up of a designer, a psychologist, and a behavioral economist at Australia's RMIT University recently introduced a new typeface, Sans Forgetica, that uses clever tricks to lodge information in your brain. The font-makers drew on the psychological theory of "desirable difficulty"--that is, we learn better when we actively overcome an obstruction. Sans Forgetica is purposefully hard to decipher, forcing the reader to focus.

On the second floor of an unassuming office building in Shibuya, Tokyo, a process of transformation is happening. "We don't want to stand out," says Hiroko Minamoto, president and co-founder of video game translation firm 8-4. The company, named after the final level of Super Mario Bros, specialises in repackaging Japanese video games for English-speaking audiences, or vice versa. "When localisation is bad, that's when it stands out, and that's when people yell at us. We want it to be natural."

Depending on whether you embrace or fear the robo-future of design, Mark Maker (via Sidebar) could be considered either the beginning of the end, or proof that such fears are overstated, because bots are still pretty crap at design. The system then uses a genetic algorithm–a kind of program that mimics natural selection–to generate an endless succession of logos. When you like a logo, you click a heart, which tells the system to generate more logos like it. By liking enough logos, the idea is that Mark Maker can eventually generate one that suits your needs, without ever employing a human designer. Mark Maker creates its logos by breaking each design in half, so that it contains both a base design and an accent element.

A version of this post originally appeared on Tedium, a twice-weekly newsletter that hunts for the end of the long tail. We live in a world where facial recognition has become so sophisticated that we're being forced to ask very serious ethical questions about it. In China, it's being used to detect toilet paper theft. But I want to take a step back from the big hairy ethical questions and consider how we started on this road--with typography. Optical character recognition, or OCR, is a technology that came up with computing in general.