| --- |
| 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| benchmarks and corpus — exploratory and superseded experiment code is not |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](https://huggingface.co/Ericu950/swedish-print-ocr-3b-benchmark) |
| - Production model: [Ericu950/swedish-print-ocr-3b](https://huggingface.co/Ericu950/swedish-print-ocr-3b) |
| - Training data + benchmarks + images: [Ericu950/swedish-print-ocr-training-data](https://huggingface.co/datasets/Ericu950/swedish-print-ocr-training-data) |
| - OCR corpus (2.26M pages): [Ericu950/litteraturbanken-ocr-corpus](https://huggingface.co/datasets/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. |
|
|
