Tesseract LSTM OCR — GGUF models for CrispEmbed
Apache-2.0 Tesseract LSTM line-recognition models converted to GGUF for CrispEmbed. The repository contains English, German, French, Spanish, Italian, Portuguese, Dutch, Russian, Arabic, Simplified Chinese, Japanese, and Korean variants.
These are line recognizers: normalize and crop individual text lines before recognition, or pair them with a detector such as DBNet for full-page OCR.
Precision variants
Each language has an F32 reference, an F16 deployment model, and Q8_0/Q4_K
variants. The quantized models preserve output.weight and output.bias at
the source precision (F16 for the multilingual F16-derived models; F32 for
German regenerated from its original .traineddata). Only recurrent matrices
are quantized. This preserves the CTC decision boundary and keeps critical
character logits stable.
| Variant | Purpose |
|---|---|
*-f32.gguf | Canonical reference and parity baseline |
*-f16.gguf | High-fidelity deployment |
*-q8_0.gguf | Recommended compact deployment |
*-q4_k.gguf | Smallest deployment; validate on the target corpus |
Usage
crispembed -m tesseract-eng-q8_0.gguf --ocr line.png
The native runtime performs height normalization and CTC greedy decoding. Word spacing and page reading order must be supplied by the surrounding OCR pipeline; the Tesseract DAWG language models are not part of this GGUF graph.
Conversion
python models/convert-tesseract-to-gguf.py \
--model eng.traineddata --output tesseract-eng-f32.gguf
crispembed-quantize tesseract-eng-f32.gguf tesseract-eng-q8_0.gguf q8_0
crispembed-quantize tesseract-eng-f32.gguf tesseract-eng-q4_k.gguf q4_k
The exact source URL/revision and SHA-256 are stored in each GGUF's metadata.
License
Apache-2.0, following the upstream Tesseract language data. Preserve the upstream attribution and source checksum when redistributing derivatives.
Provenance and EU AI Act Art. 53 note
- Upstream model: tesseract-ocr/tessdata.
- Upstream licence:
apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not. - What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
- Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
- Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.