Instructions to use inviolus/InviolusOCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use inviolus/InviolusOCR with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://inviolus/InviolusOCR") - Notebooks
- Google Colab
- Kaggle
Card: point to the newer D version on GitHub
Browse files
README.md
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This replaces [inviolus/erdem-ocr-tr-tinyml](https://huggingface.co/inviolus/erdem-ocr-tr-tinyml)
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(2026-10-09), whose int8 file lost most of its accuracy.
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## Files
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| File | What it is |
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This replaces [inviolus/erdem-ocr-tr-tinyml](https://huggingface.co/inviolus/erdem-ocr-tr-tinyml)
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(2026-10-09), whose int8 file lost most of its accuracy.
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> **Newer version on GitHub (2026-11-01):** a continuation of this model, *D*,
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> trained with 47,613 extra printed-document lines (donut 200k, CC BY-SA 4.0),
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> reads 46.5% of the donut test rows exactly as int8 (this model: 9.3%), with
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> the same size and 1 to 2 points less on the sets below. The files here are
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> still the 2026-10-31 model. Scores: [InviolusOCR results](https://github.com/ErdemWilkinson/InviolusOCR#2026-11-01-d-b-plus-the-donut-200k-lines).
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## Files
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| File | What it is |
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