linear-a-linear-b-bothros

BOTHROS — Linear A & Linear B sign reading from photographs

Weights for the BOTHROS pipeline: photograph an ancient Aegean tablet, get the signs on it by catalogue code and reading.

🤗 Try the live demo — no install, upload a photo. (Free-tier Space; if it shows "sleeping", give it ~30s to wake.)

The name — a bóthros (βόθρος) is the pit Odysseus digs in the Odyssey, pouring libations so the spirits of the dead rise to speak with him. Apt for a tool that reads scripts silent for three thousand years.

• yolo_aegean_unified.pt — one YOLO11s detector localising signs for both scripts (sign detection is class-agnostic; Linear A and Linear B signs are visually cognate).
• la_classifier.pth / lb_classifier.pth — ConvNeXt-Tiny classifiers (AB-codes for Linear A; B-codes + readings for Linear B).
• lb_class_to_reading.json — Linear B B-code → phonetic reading map.

Scope: this release covers Linear A and Linear B. Cretan Hieroglyphic (a stronger internal result, held back over train/test leakage in too small a corpus) and Cypro-Minoan (parked — the comparable Corazza 2022 corpus is non-redistributable) are not in v0.1.0; see the GitHub repo for status.

Results (held-out, leak-free)

Per-line F1 is at the precise operating points (conf-filter 0.25 LA, n=133 / 0.30 LB, n=320). DeepScribe is a cross-domain reference (different script/corpus, hand-annotated GT, 141 classes vs LA 374 / LB 142), not a head-to-head. Full methodology + reproduction: GitHub repo.

Cross-script: a Linear-B-only detector reads Linear A at 60.7% F1 zero-shot — the basis for shipping one unified aegean-unified detector for both scripts.

Benchmark vs release weights

Two sets ship here. Benchmark (yolo_aegean_unified.pt, la_classifier.pth, lb_classifier.pth) — strict held-out split; the numbers above are theirs; use these to reproduce/compare. Release (*_release) — retrained on the full data incl. the held-out split: max capability + broader coverage (LB 148 vs 142 classes), but NOT benchmarkable (they have seen the test tablets — cite the benchmark numbers, not these). Fetch with download_weights.py --release; run with bothros read … --release.

Usage

pip install bothros            # or: pip install -e . from the GitHub repo
python3 scripts/download_weights.py
python3 -m bothros read your_tablet.jpg --script la   # or --script lb

Licence

CC BY-NC-SA 4.0 — derived from research-only corpora: lineara.xyz + GORILA (Linear A images), SigLA (Ester Salgarella & Simon Castellan) + lineara.xyz (Linear A sign boxes + AB-code catalogue), DĀMOS (Federico Aurora) + LinearBExplorer (Linear B). No corpus images are redistributed — only the trained weights. The pipeline source code is MIT (see the GitHub repo). Non-commercial use only.

Citation

DOI 10.5281/zenodo.20746759 · code + docs: https://github.com/jmacdonald263/bothros