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Organization

Kaa ML

kaa-ml

Models in Library2
Datasets in Library0
Models on Hugging Face2
Followers7

Compare the published evidence

This profile connects the 2 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.

Published tasks: Image to text.

Reported licenses: apache-2.0. Open a record to review its specific license and conditions.

Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.

Models

Model · Image to text

tesseract-kaa-cyrl

Kaa ML

Tesseract 5 LSTM model for printed Karakalpak in the Cyrillic alphabet, including the letters Ә Ғ Қ Ң Ө Ү Ў Ҳ. Fine-tuned from Tesseract's Kazakh kaz (tessdatabest). For Latin-script Karakalpak use kaa-ml/tesseract-kaa-latn (CER 4.7%). Before this model, the usual choice for Karakalpak Cyrillic was the Kazakh model. It has no ў and ҳ, so it silently writes у and х instead — the text looks right but is misspelled. The Russian model loses all eight Karakalpak letters. 10 scanned pages of an old printed Karakalpak book (never used in training), --psm 4. CER = character error rate (lower is better). Letter recall = share of ә ғ қ ң ө ү ў ҳ in the reference that the model output contains. \ on…

Open weights apache-2.0 tesseract

Model · Image to text

tesseract-kaa-latn

Kaa ML

Tesseract LSTM model for printed Karakalpak in the current Latin alphabet, including á ǵ ı ń ó ú. Fine-tuned from Tesseract's English model, with a Karakalpak word list, punctuation and number dictionaries built in. For Cyrillic Karakalpak use kaa-ml/tesseract-kaa-cyrl. 12 pages from digital Karakalpak (Latin) academic PDFs, rendered at 300 DPI; reference = the PDF's own text layer. --psm 3. CER = character error rate (lower is better). Letter recall = share of á ó ú ı ń ǵ in the reference found in the output. These are clean digital documents; old or poor scans will score worse. Speed: ~3.5 s per A4 page on a MacBook CPU. Fine-tuned in 2023 from eng (Tesseract 4.1.1 format, works with…

Open weights apache-2.0 tesseract