ewe.traineddata is an experimental Tesseract 5 recognition model for Ewe (ee / ISO 639-3 ewe). It was fine-tuned from the official high-accuracy Latin.traineddata model using deterministic, synthetically rendered Ewe text lines.
This is a v0.1 research preview. Its reported error rates measure recognition of held-out synthetic line images. They do not measure end-to-end performance on real scanned pages, page segmentation, layout analysis, reading order, tables, or handwriting.
Evaluation
The 40,000-line training collection was deterministically divided by the official tesstrain workflow into 36,000 optimization lines and 4,000 internal evaluation lines. The final model was re-evaluated with Tesseract 5.5.2 lstmeval:
BCER eval=1.119, BWER eval=3.009
Measurement
Lines
BCER
BWER
Final training estimate
36,000
1.146%
3.175%
Held-out synthetic evaluation
4,000
1.119%
3.009%
BCER is Tesseract's character error rate and BWER is its word error rate. Lower is better. These values must not be presented as accuracy on real PDFs.
The list.eval file and line-image training data are not distributed in this model repository pending a source-data rights review. The exact result and environment are recorded in metrics/evaluation.json; the complete original console output is in metrics/training.log.
Training error curve
Training
Tesseract/lstmtraining: 5.5.2
Official tesstrain commit: 405346a3a67d8e4e049341d1da6a4b752e0b8351
The synthetic renderer used only fonts verified to contain every required Ewe character. Configurable degradation covered blur, skew, contrast, noise, JPEG compression, and faded print. See config/pipeline.json and config/tesstrain.mk.
For PDFs, render each page to PNG or TIFF first. Page segmentation mode must be selected for the document layout; --psm 3 is automatic page segmentation, --psm 6 assumes one text block, and --psm 13 treats the image as one line.
Intended uses
Research on printed Ewe OCR
Recognition of already-segmented Ewe text lines
Prototyping searchable text extraction from printed Ewe documents
Fine-tuning and evaluation by low-resource-language researchers
Limitations
No manually transcribed real-scan CER/WER benchmark has been completed.
The model was trained on synthetic line images, so its reported metrics are optimistic for real documents.
Page segmentation and reading order were not trained or measured by the line recognizer.
OCR output should be reviewed by an Ewe reader before use in consequential settings.
Tesseract confidence values are not accuracy percentages.
Files
ewe.traineddata: deployable Tesseract model
checkpoints/ewe_checkpoint: final continuation checkpoint
metrics/training.log: complete tesstrain log
metrics/training-curve.png: training-error curve
metrics/evaluation.json: machine-readable evaluation record
config/: deterministic preprocessing and training configuration
SHA256SUMS: artifact integrity checks
License and data notice
The starting Tesseract/tessdata model is distributed under Apache-2.0. The source corpus, PDFs, extracted text, and rendered training images are not included here. The license for this fine-tuned research-preview artifact is currently marked other pending a documented review of all training-corpus rights. See LICENSE.md before redistribution or commercial use.
Citation
A paper citation is not yet available. Until then, cite this repository, model version v0.1.0, and its immutable Hugging Face commit hash.
