Contriver & MudTablet
Contriver Contriver
MudTablet MudTablet
Contriver Contriver
Hey MudTablet, I just sketched a prototype that could read those ancient carvings and turn them into digital data. Think of it as a stone‑based memory chip. What do you think? Any chance those marks hold more than just ink?
MudTablet MudTablet
That sounds like a nice toy, but if you want something more than a fancy memory bank, you have to get the actual symbols right first. Those carvings aren't just ink; they’re a language, and without parsing the glyphs exactly you’re just turning stone into meaningless data. If you can nail the syntax, the semantics will follow. Otherwise, you’ll be feeding a machine a meaningless string of symbols.
Contriver Contriver
Right, parsing is the crux. I’m already drafting a glyph‑recognition algorithm that tags every line, measures curvature, and assigns phonetic weights. Once the syntax tree is stable, the semantics will naturally cascade. I’ll need a decent dataset of confirmed symbols to train it, though. Any leads on that?
MudTablet MudTablet
You’ll have to dig into the archives, not just scan the stones. Look at published corpora from the Epigraphic Society, the National Museum’s digitized collections, and the university’s open‑access corpus of Linear A and B. Those datasets already have glyph IDs and transliterations. If you can get a few hundred confirmed signs, that’s a start, but you’ll need a balanced set across styles and periods to avoid bias. The bigger the training set, the more reliable the curvature and phonetic weighting will be.
Contriver Contriver
Sounds like a data‑mining marathon. I’ll start pulling those corpora, tag the glyph IDs, and create a balanced training set—maybe a thousand signs? That’ll give my curvature engine enough variety to learn the style shifts. If I can line them up with the transliterations, the syntax tree will begin to shape itself. Stay tuned for the first prototype!
MudTablet MudTablet
A thousand signs is a respectable target, but remember that a thousand that all look the same still won’t teach the engine about context. Gather the oddities, the rare strokes, the marginal variations—you’ll need those quirks to map the syntax tree reliably. Good luck, and don’t let the data get lost in a pile of inked noise.
Contriver Contriver
Got it, I’ll hunt for the outliers too—those weird strokes that throw the parser off. I’ll build a “quirk list” to train the engine on context, not just the textbook shapes. Thanks for the heads‑up, and I’ll keep the data from getting swallowed by a black hole of ink.We complied.Got it, I’ll hunt for the outliers too—those weird strokes that throw the parser off. I’ll build a “quirk list” to train the engine on context, not just the textbook shapes. Thanks for the heads‑up, and I’ll keep the data from getting swallowed by a black hole of ink.