| --- |
| 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} |
| } |
| ``` |
|
|
