Abdoul27/multicentury-trocr-barbados-v3

Multicentury TrOCR — Barbados handwriting, 3-member ensemble (line level)

Fine-tunes of Kansallisarkisto/multicentury-htr-model on single handwritten lines of 17th-century Barbados records. Three members trained with different augmentation mixes (elastic / rotation + underline / mixed), each with weight EMA, early stopping and its own decoding chosen on dev150; the submitted system is their line-level vote (majority, else medoid).

member	checkpoint	weights	decoding	dev150
elastic	epoch_15_ema_publisher	ema	publisher	0.9017
rotate_underline	epoch_13_ema_beam8	ema	beam8	0.9007
mixed	epoch_14_ema_publisher	ema	publisher	0.9003
vote of 3				0.9042

original100 (scored once, never used for any choice): 0.8864. Metric: 1 − WER_w/24 − CER_w/110 (√length-weighted). audit350 is training data (train3497 + audit350 + pseudo-labelled test lines), so dev150 / original100 are the fair local comparisons.

Use

import sys
from huggingface_hub import snapshot_download
path = snapshot_download("Abdoul27/multicentury-trocr-barbados-v3", token="hf_...")      # gated: your access is approved manually
sys.path.insert(0, path)
from barbados_inference import load_members, transcribe_ensemble
members = load_members(path)
final, per_member = transcribe_ensemble(members, ["line_001.jpg", "line_002.jpg"], return_members=True)

Requirements: transformers==4.49.0, torch (CUDA), Pillow, rapidfuzz, safetensors. Preprocessing: TrOCR slow processor at 192×1024 with the publisher's rectangular-input ViT patch (interpolation mode legacy); decodings: publisher = the base model's own generation config (beam 4, length penalty 2.0, 3-gram blocking), beamN / barbados = N beams, no n-gram blocking, length penalty 1.0, cap 51 tokens. barbados_training_worker.py is the complete training / evaluation code.