Language support
What each model's own card claims about language coverage — compiled from the model cards as of 2026-07-15. Treat these as claims, not guarantees: only one model (Surya) publishes per-language scores, and a "multilingual" tag tells you very little. Machine-readable version (plus params, backend, image pins): models.json.
The fastest way to find out whether a model handles your language is to run it on a few of your own pages — --max-samples 10 costs a few cents on a T4/L4:
hf jobs uv run --flavor l4x1 -s HF_TOKEN \
https://huggingface.co/datasets/uv-scripts/ocr/raw/main/surya-ocr.py \
your-dataset your-output --max-samples 10
Sorted by strength of evidence, then coverage:
| Model | Card claim | Evidence |
|---|---|---|
| Surya OCR 2 | 91 languages | The only per-language benchmark published (full table): 38/91 score ≥90%, 76/91 ≥80%. Weakest of the top-15: Arabic 72.7%, Vietnamese 73.2% |
| dots.ocr | 100 languages | Explicit low-resource claim, backed by an in-house benchmark (1,493 PDFs across 100 languages) — aggregate scores only, not per-language |
| Tesseract 5 | 125 traineddata packs (~100+ languages + script models) | Fully enumerable (tessdata_best) — the broadest named coverage here, incl. many low-resource languages; script models (Latin, Cyrillic, Devanagari, …) cover languages without a dedicated pack |
| Qianfan-OCR | 192 languages | Headline claim; no list or per-language numbers |
| PaddleOCR-VL / 1.5 / 1.6 | 109 languages; 1.5 adds Tibetan + Bengali | Names scripts (Cyrillic, Arabic, Devanagari, Thai) and claims handwriting + historical documents; no per-language numbers |
| PP-OCRv6 | 48 languages | Official card claim |
| Nanonets-OCR2-3B | 11 named + "many more" (incl. Arabic + CJK) | Illustrative list, no benchmark — but the only card claiming multilingual handwriting |
| LightOnOCR-2-1B | 11 (9 European + zh/ja) | Declared, not benchmarked. v1 is explicitly European/Latin-script only (9) |
| GLM-OCR | 8 (zh en fr es ru de ja ko) | Declared in card metadata only; no per-language evidence in the card body |
| HunyuanOCR-1.5 | "Multilingual" | Nothing enumerated, but explicitly targets low-resource + ancient-script OCR as a design goal (new in 1.5; the pinned 1.0 revision doesn't claim this) |
| dots.mocr · DeepSeek-OCR / -2 · Unlimited-OCR · NuExtract3 | "Multilingual", unspecified | A tag or one-liner only — no count, list, or benchmark |
| olmOCR-2 · Nanonets-OCR-s · SmolDocling · LFM2.5-VL-Extract | English only | Stated on the card |
| Falcon-OCR · OvisOCR2 · FireRed-OCR · ABot-OCR · RolmOCR · NuMarkdown-8B · lift | Not stated | No language information on the card at all |
Text-only extraction: LFM2-1.2B-Extract (chains after OCR) names 9 languages: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Spanish.
Reading the table for low-resource work
- Only Surya lets you check your language before running anything — its benchmark table has a row per language.
- Tesseract's coverage is enumerable — if your language has a traineddata pack, it's supported (quality varies; it's the legacy baseline, not the quality leader).
- dots.ocr and HunyuanOCR-1.5 are the VLMs that talk about low-resource languages at all; PaddleOCR-VL-1.5 names Tibetan and Bengali specifically.
- A big claimed number (192, 109) without a list or per-language scores means you're testing it yourself either way — which is cheap (see the command above).