achimrabus/crnn-ctc-church-slavonic

Church Slavonic HTR Model (Puigcerver CRNN)

A Handwritten Text Recognition (HTR) model for Church Slavonic manuscripts, based on the CNN + BiLSTM + CTC architecture introduced in Puigcerver (2017) and used as the backbone of PyLaia and Transkribus.

This is a clean-room PyTorch reimplementation of that published architecture (PyLaia-inspired). It does not use the PyLaia Python package and is not loadable by it — training and inference run via plain PyTorch (see Usage below).

Model Details

• Architecture: CNN encoder [12, 24, 48, 48 filters] + 3-layer Bidirectional LSTM (256 units) + CTC decoder (Puigcerver 2017)
• Input: Grayscale line images, normalized to 128 px height with aspect ratio preserved
• Output: UTF-8 text (Church Slavonic characters including titlos, abbreviation marks, and diacritics)
• Vocabulary: 152 symbols (symbols.txt)
• Framework: Pure PyTorch — clean-room reimplementation of the Puigcerver (2017) architecture (PyLaia-inspired); the PyLaia package is not required

Performance

Metric	Value
Validation CER	2.89%
Training epochs	59
Training lines	309,959
Training pages	2,643
Validation lines	20,679
Validation pages	205

Training Data

Trained on Church Slavonic handwriting images transcribed and exported from Transkribus (see the corresponding Transkribus model page). The dataset covers Old Cyrillic script styles (uncial and semi-uncial), primarily East Slavic with South Slavic material included.

Source manuscripts:

• Codex Suprasliensis (10th–11th c., South Slavic recension)
• Catecheses of Cyril of Jerusalem (transmitted version: 11th c., East Slavic recension)
• Methodius of Olympus: Symposion (transmitted version: 17th c., East Slavic recension)
• Velikie Minei Četʹi (16th c., East Slavic recension): large parts of the March and May volumes, Apostolos from the June volume

Our CRNN-CTC model was trained on the full collection: 309,959 training lines (2,643 pages) and 20,679 validation lines (205 pages), exported from Transkribus.

The Transkribus model was trained by Elena Renje as part of the QuantiSlav project and curated by Achim Rabus (Slavic Department, University of Freiburg).

Usage

Requirements

The inference code lives in polyscriptor and imports other modules from it, so run it from a clone of the repository. Install into a fresh virtual environment in one pip call; requirements-kraken.txt adds the Kraken segmentation used for full pages below (for a GPU install, see the polyscriptor README):

git clone https://github.com/achimrabus/polyscriptor
cd polyscriptor
python3 -m venv htr_env && source htr_env/bin/activate
pip install -r requirements.txt -r requirements-kraken.txt
hf download achimrabus/crnn-ctc-church-slavonic --local-dir models/crnn-ctc-church-slavonic

Inference

From the root of the clone:

from inference_pylaia_native import PyLaiaInference
from PIL import Image

# Load model
model = PyLaiaInference(
    checkpoint_path="models/crnn-ctc-church-slavonic/best_model.pt",
    syms_path="models/crnn-ctc-church-slavonic/symbols.txt"
)

# Transcribe a line image
image = Image.open("line_image.jpg")
text, confidence = model.transcribe(image)
print(text, f"(confidence {confidence:.2f})")

Note: Input should be a single text line image, not a full page. Preprocessing (grayscale conversion, height normalization, aspect ratio preservation) is handled automatically by inference_pylaia_native.py.

For full-page inference with automatic line segmentation, use batch_processing.py. The neural Kraken segmenter (kraken-blla) is strongly recommended; the default projection-based segmenter (hpp) is fast but can miss most lines on complex pages:

python batch_processing.py \
    --engine crnn-ctc \
    --model-path models/crnn-ctc-church-slavonic/best_model.pt \
    --segmentation-method kraken-blla \
    --input-folder images/ \
    --output-folder output/

Web Interface (recommended)

polyscriptor's main interface runs in the browser: upload page images or PDFs, segment them automatically (Kraken) or reuse existing PAGE XML, transcribe, correct lines inline and export TXT, CSV or PAGE XML. It runs on a laptop or on a remote server (via SSH tunnel), and CRNN-CTC models also work without a GPU.

The web interface picks up any folder under models/ that contains best_model.pt and symbols.txt, so after the download above it only needs to be started:

uvicorn web.polyscriptor_server:app --host 0.0.0.0 --port 8765
# open http://localhost:8765 and choose the model under CRNN-CTC

Desktop GUI

polyscriptor also ships PyQt6 desktop interfaces: transcription_gui_plugin.py for interactive single pages (automatic line segmentation, PAGE XML export) and polyscriptor_batch_gui.py for whole folders (uses existing PAGE XML files, e.g. from Transkribus, when available).

Intended Use

• Transcription of Church Slavonic historical manuscripts
• Research in Slavic medieval studies and digital humanities

Limitations

• Full-page segmentation quality depends on the segmentation method used upstream

Citation

If you use this model in your research, please cite the architecture paper and this model:

@article{puigcerver2017multidimensional,
  title     = {Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition?},
  author    = {Puigcerver, Joan},
  journal   = {Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)},
  year      = {2017},
  url       = {https://www.jpuigcerver.net/pubs/jpuigcerver_icdar2017.pdf}
}

@misc{rabus2026polyscriptor,
  title  = {Polyscriptor: Multi-Engine HTR Training \& Comparison Tool},
  author = {Rabus, Achim},
  year   = {2026},
  url    = {https://github.com/achimrabus/polyscriptor}
}