Greek Alphabet Cards

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Greek Alphabet Cards — Random Quark Labs

Random Quark<br>Labs / Greek Alphabet Cards

Side Project · 2026

Greek Alphabet<br>Cards

A set of cards I made to help my kids learn the Greek alphabet through<br>visual associations — each object is drawn so that it looks like the<br>letter its name begins with.

The Idea

We live abroad in China, and Greek is one of three languages my kids are<br>learning. They were three and a half when I started these cards about<br>five months ago, so I wanted something playful to nudge them along.

My first attempt was an “A for airplane”-style deck — pictures<br>of objects whose names start with each letter. After printing version<br>one, I had the epiphany:

What if the object didn’t just start with the letter — what if<br>it looked like it too?

The shape of the letter pulls up the object, and the object’s name pulls up the letter. Research seems to<br>back this up — kids learn the alphabet far faster this way than by rote.

Finding the Objects

In the beginning I relied on my memory to come up with these associations,<br>but you run out of ideas very quickly. So I got a bit more organised<br>about it:

Dictionary<br>I downloaded an entire Greek dictionary that contained not just the<br>words but also a list of words per letter, along with the frequency<br>with which each word appears in Greek text. I used<br>GreekLex,<br>which contains 35,304 Modern Greek words ranging in length between 1<br>and 22 letters.

greeklex.csv · excerpt

IDnr<br>Word<br>Length<br>LemmaFreq<br>WordFreq

223<br>φως<br>125<br>90.2

224<br>χαν<br>7.4

225<br>χαφ<br>14<br>13.5

226<br>χολ<br>5.6

227<br>χοπ<br>2.8

228<br>ψες<br>0.1

229<br>ψιτ<br>0.2

230<br>ωδή<br>1.4

231<br>ωθώ<br>17

232<br>ώρα<br>549<br>335.8

233<br>ώση<br>3.6

234<br>αβάς

235<br>αγάς<br>0.1

236<br>άγια<br>1.9

237<br>αγνή<br>3.2

238<br>άγος<br>1.1

239<br>άγρα

240<br>αγώι<br>0.1

241<br>αδάμ<br>4.4

242<br>άδης<br>0.4

243<br>αέρι

244<br>αθώα<br>5.7

245<br>αίγα

A small slice of the GreekLex corpus. Each row is a word with its length<br>in characters, its lemma frequency (how often the base form<br>occurs in the corpus) and its word frequency (how often this<br>exact surface form occurs). The frequency columns are what let me<br>filter for words a child would plausibly recognise.

Filtering<br>I ran a filter to keep only words that were:

between 3 and 10 characters long, and

had a frequency of at least 100 in the corpus, so they wouldn’t be too rare.

The aim was a vocabulary that my kids would plausibly know.

Visual candidate generation<br>That still left 50 to 2,500 words per letter — too many to eyeball. So<br>I fed them to ChatGPT in batches of 50, asking for each one whether<br>its referent could be drawn to echo the letter’s shape, and how. The<br>list usually came back down to 10–200 candidates. Ω Omega<br>was the extreme case — essentially no match. Most suggestions were<br>weak, but every batch had a few good ones.

triage · letter ε · sample output

›ελιά — an<br>olive tree can be stylised with a vertical trunk on the left and three rounded clusters/branches extending to<br>the right, echoing the three arms of ε<br>›ελαία — same<br>idea as ελιά: a small olive tree with a slim trunk and three leafy bulges to the right can read clearly as<br>›ελάφι — a<br>deer’s head in profile could be stylised so the neck forms the spine of ε and the snout, chest, and lower jaw<br>create the three outward curves

Sample of what ChatGPT returned for one of the letter ε batches. Each line<br>is a candidate word with a suggestion for how its referent could be drawn<br>to echo the shape of the letter. Most suggestions weren’t usable, but<br>a handful in every batch were genuinely promising.

Image generation<br>From the much smaller shortlist, I tried examples in OpenAI’s image<br>generation (gpt-image-1.5) and experimented until I got<br>the images I used on the cards. To improve visual match, I also gave<br>the image model an image file of the Greek letter so that it could<br>keep it in its “mind” while generating the object.

Input<br>prompt to image model

generate an image of a lion seen sideways, sitting back on its rear<br>legs and looking slightly upward, with the front legs supporting the<br>body in a graceful diagonal posture inspired by the greek letter λ.<br>The mane and neck can softly curve near the top while the tail<br>gently trails downward to the right, creating only a subtle visual<br>echo of λ. The composition should feel like a natural, believable<br>lion pose rather than a forced typographic construction. I am<br>attaching the image of the letter so that you can use it.

Attachment<br>lambda.png · reference letter

Output<br>image returned by model

Some cases were a bit more stubborn. For example, as much as I tried<br>prompting it to make an image of a snake (φίδι in Greek) that<br>looks like the letter φ (phi), it just wouldn’t do it<br>correctly. In the end I drew a snake by hand and asked it to render it<br>in the appropriate style:

My hand sketch · input

Rendered card · output

Card layout<br>I wrote some code to lay the text out on the cards.

Two Card Sets

There are two sets:

Object cards — each card shows an object whose name<br>starts with a particular Greek letter, drawn so that it resembles the<br>letter. On...

letter greek image cards object alphabet

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