license: mit
language:
- sv
tags:
- ocr
- historical
- fraktur
- swedish
pretty_name: Swedish Print OCR Code
Swedish Print OCR — training and evaluation code
The code behind the paper A Vision–Language OCR Model and an Open Corpus (Cullhed). This is the curated pipeline that produced the released models, benchmarks and corpus — exploratory and superseded experiment code is not included. Comments and docstrings are in Swedish; they document not just what each script does but why it is built the way it is.
Companion repositories
- Evaluation model: Ericu950/swedish-print-ocr-3b-benchmark
- Production model: Ericu950/swedish-print-ocr-3b
- Training data + benchmarks + images: Ericu950/swedish-print-ocr-training-data
- OCR corpus (2.26M pages): Ericu950/litteraturbanken-ocr-corpus
Layout, mapped to the paper
data/ — §2 Training data
Source acquisition (fetch_runeberg.py, refresh_runeberg_text.py,
add_spraakbanken.py, extract_spraakbanken_regions.py, add_external.py),
repair and deduplication (repair_runeberg.py, dedup_runeberg.py),
per-work language and typeface classification (detect_language.py,
classify_typeface.py, train_typeface_cnn.py, typsnitt_verk.py),
integrity checks (scan_images.py, audit_dataset.py,
kolla_traningspar.py), and the final seeded mixture build
(build_dataset_final.py — 34% roman / 44% Swedish blackletter / 22% German
blackletter, convention and century instructions).
cleaning/ — §2, page–text alignment screen
The three-step segmentation check used to remove pages whose ground truth
demonstrably belongs to another page: segment (segmentera_pool.py), read
each segment (las_koord.py), align against ground truth (radpar.py), then
filter the training mixture on page similarity (rensa_train.py).
benchmark/ — §3 Evaluation
Work-disjoint, period-stratified principal benchmark (build_testset.py),
the character ten-gram leakage check that removes same-text-different-title
editions (find_text_leakage.py), and the external Wikisource benchmark
(fetch_wikisource.py, build_extern_wikisource.py,
build_extern_dataset.py) with its cross-system reference-outlier screen
(rensa_extern.py).
training/ — §4 Model
The SLURM training launcher (train_chain.sbatch): CHURRO-3B base, frozen
vision encoder, lr 8e-5 cosine, effective batch 128, bfloat16, sequence
packing, on 4× GH200.
eval/ — §4.1 and §5
The single scoring program used for every system (eval_testset.py,
eval_cer.py), repetition-loop detection (loopvakt.py) backing the uniform
decoding repair, baseline runners under identical conditions (run_kraken.py,
run_surya.py, run_tesseract.py, run_trocr.py, run_got_ocr2.py,
run_calamari.py + calamari_lines.py), and results aggregation
(sammanstall.py).
corpus/ — §6 Released corpus
The production pipeline over Litteraturbanken's facsimile collection:
work listing and download (lista_verk.py, lb_api.py), volume OCR
(ocra_volymer.py), page-count control and gap filling
(granska_sidkarta.py, komplettera.py), repair of looping/truncated pages
(loopplan.py, las_om_loopar.py), page-level licence classification for
split-licence volumes (klassa_apparat.py), verification of page naming
against Litteraturbanken's proofread e-text (hamta_etexter.py,
kolla_justering.py), and corpus assembly and release filtering
(bygg_textkorpus.py, slapp_ur_textkorpus.py).
Environment
Python 3.12 on aarch64 (NVIDIA GH200), trained with ms-swift; inference via
vLLM. Paths in the scripts refer to the original project layout
(dataset/, external/, work/ under a project root) — pair with the
training-data repository's image shards, which preserve those relative paths.