Wadjet

Point a phone at a hieroglyphic inscription and Wadjet reads the signs back to you. The interesting part is not the reading. It is that the models do not run on the server.

I trained three classifiers, exported them to ONNX, and ran them client-side through ONNX Runtime Web. Three things follow. The photo never leaves the device, which is a real property for somebody standing in a museum rather than a line of marketing. Inference costs the server nothing, so the app survives on a free tier instead of falling over. And it keeps working on a bad connection, because the model is already cached.

The hieroglyph classifier reports 98.2%a across 171 Gardiner sign classes. The landmark classifier reports 93.8% across 52 Egyptian sites. Both figures come from the repo's own README. The test set composition is not published there, so read them as reported model accuracy, not as a benchmark result.

Before this, there was Horus AI

The first version got to ~80%b, up from 50%. What moved it was not a bigger model.

The Sphinx was being classified under the pyramid complex, because the two classes were not meaningfully distinct in the data. Merging confusing classes, balancing the rest, and checking labels by hand did the work. The taxonomy was wrong before the model was.

Wadjet is what I built once I knew that.