Release DeepOCR v3

README.md

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+---
+license: apache-2.0
+library_name: pytorch
+pipeline_tag: image-to-text
+tags:
+  - ocr
+  - htr
+  - handwritten-text-recognition
+  - historical-documents
+---
+
+# DeepOCR v3
+
+A line-level text recognition model for historical print and handwriting: Fraktur, Antiqua, Kurrent, and medieval and modern Latin script, in one checkpoint. Code, training and architecture: [DeepOCR](https://github.com/m2key1/DeepOCR).
+
+## Files
+
+| File | What it is |
+|---|---|
+| `deepocr-v3.pt` | The model weights (EMA), with every architecture setting stored inside |
+| `union_bytes_v3.bin` | Byte-level 6-gram KenLM, fused into the beam search |
+| `union_bytes_v3.dict` | The LM's token list, index-parallel to the model's 261-token byte vocabulary |
+
+The LM belongs to this checkpoint. Do not pair it with another one.
+
+## Use
+
+```python
+from pathlib import Path
+
+import torch
+from PIL import Image
+import infer
+from infer import load_model, transcribe_beam
+
+infer.KENLM_PATH = Path('union_bytes_v3.bin')
+infer.LM_DICT_PATH = Path('union_bytes_v3.dict')
+infer.LM_WEIGHT = 1.0
+infer.BEAM_WIDTH = 10
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model, tokenizer = load_model('deepocr-v3.pt', device)
+print(transcribe_beam(model, tokenizer, [Image.open('line.png')], device)[0])
+```
+
+Input is one text line as an RGB image; it is scaled to 256 px high and read up to 6144 px wide. Three flags choose the transcription convention: `long_s` (ſ), `oblique_hyphen` (⸗) and `typographic_punctuation`. Set them in `infer.CONVENTION`; all off by default.
+
+The model is also the OCR builtin of [DeepLabel](https://github.com/m2key1/DeepLabel).
+
+## Model
+
+170.5M parameters. A grouped CNN backbone feeds four mixture-of-experts transformer layers (64 experts, 8 active per column, plus one shared expert) and a CTC head over bytes, so it can emit any Unicode character. Trained from scratch.
+
+## Evaluation
+
+CER on the held-out validation split, 25,000 lines:
+
+| Decoding | CER |
+|---|---|
+| Greedy | 3.68% |
+| Beam 10 | 3.57% |
+| Beam 10 + KenLM, weight 1.0 | 3.03% |
+
+## Training data
+
+Public datasets of historical print and handwriting. The language model was built from the same training split.
+
+## Limitations
+
+- Reads single lines only; it needs a line detector or segmentation in front of it.
+- Reads best on material like its training data: European print and handwriting from the medieval to the modern period. Accuracy drops on other scripts and unusual hands.
+- The language model favours the training text; for mostly numeric or tabular text, lower `LM_WEIGHT` or decode without it.
+
+## License
+
+Apache-2.0.
+
+## Citation
+
+```bibtex
+@software{fleischhacker_deepocr,
+  author = {Fleischhacker, David},
+  title  = {DeepOCR},
+  year   = {2026},
+  url    = {https://github.com/m2key1/DeepOCR}
+}
+```

deepocr-v3.pt

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+size 702599699

union_bytes_v3.bin

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+oid sha256:fbe2c7c470c0df29ab24e784ca82e80858b8d3a7e87f8e4852ade39fa5a7d92c
+size 85627362

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