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achimrabus/crnn-ctc-russian

AI model by achimrabus

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A Handwritten Text Recognition (HTR) model for 18th–20th century Russian manuscripts, printed books and typewritten documents, 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). The model covers both pre-reform (pre-1918) and modern Russian orthography: about a quarter of the training lines show ѣ, і, ѳ or word-final ъ, and all four are part of the vocabulary. The error distribution is strongly skewed: most lines are transcribed perfectly or near-perfectly, while a minority of hard lines carries a large share of the total character errors. The corpus CER above is therefore a poor predictor of quality on any single clean document,…

achimrabus/crnn-ctc-russian on Hugging Face A short preview, not the full document.

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Inside the original model card — Document outline
  1. Russian HTR Model (Puigcerver CRNN)
  2. Model Details
  3. Performance
  4. Training Data
  5. Usage
  6. Requirements
  7. Inference
  8. Web Interface (recommended)
  9. Desktop GUI
  10. Intended Use
  11. Limitations
  12. Citation

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Model overview

achimrabus/crnn-ctc-russian is a task-unspecified model repository published by achimrabus on Hugging Face. The source reports the custom library.

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Model facts

Task
Not reported
Library
custom
Recent downloads (30 days)
0
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
Not reported
License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-10-06T06:08:12.000Z
Last modified
2026-10-06T06:08:18.000Z

Compatibility and lineage

Reported languages: ru

Base-model lineage was not included.

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Source: Hugging Face Hub. Metadata observed 2026-10-06T06:39:56.505Z. Daily imports are snapshots, not real-time monitoring.

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