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LightOnOCR-2-1B: a lightweight high-performance end-to-end OCR model family

Community Article Published January 19, 2026
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Said Taghadouini's avatar
Said Taghadouini
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LightOn AI's avatar lightonai
Adrien Cavaillès's avatar
Adrien Cavaillès
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LightOn AI's avatar lightonai
Baptiste Aubertin's avatar
Baptiste Aubertin
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LightOn AI's avatar lightonai

Overview

We’re releasing LightOnOCR-2-1B, our second-generation 1B-parameter, end-to-end vision-language OCR model optimized for state-of-the-art conversion of document pages (PDF renders) into clean, naturally ordered text without relying on multi-stage pipelines. Alongside transcription, it can also output bounding boxes for embedded figures/images for workflows that need lightweight layout cues. LightOnOCR-2 is released under the Apache 2.0 license, together with a small family of open-weight checkpoints (OCR-focused and bbox-capable variants, plus base checkpoints) that can be used by the community for fine-tuning, domain adaptation, and layout-oriented applications.

01/26 UPDATE: The paper is now out! It includes the full training recipe including data/normalization pipeline, RLVR, and merging details. Read it here

Quick hits:

  • Better OCR: LightOnOCR-2-1B improves substantially over our first version LightonOCR-1B-1025 and is now State-of-the-art on OlmOCR bench, outperforming Chandra-9B by more than 1.5 percentage points overall, while being close to 9 times smaller, and without relying on pipelines.
  • Speed: 3.3× faster than Chandra OCR, 1.7× faster than OlmOCR, 5× faster than dots.ocr, 2× faster than PaddleOCR-VL-0.9B, 1.73× faster than DeepSeekOCR
  • Model family: we’re also releasing additional checkpoints, including bounding-box variants (for embedded image localization) and base checkpoints intended for fine-tuning / merging / post-training recipes.
  • Training datasets: We release two open annotation datasets used during training: lightonai/LightOnOCR-mix-0126 comprising more than 16M high-quality annotated document pages, and lightonai/LightOnOCR-bbox-mix-0126 made of close to 500k high-quality annotations including bounding boxes for figures and images.

Links

Capabilities

LightonOCR-2-1B shows significantly improved overall performance, thanks to better annotation quality, consistency, and scale; a more diverse dataset focused on European languages with an increased emphasis on scans and robustness to image degradation; and dedicated procedures to reduce looping. We provide here some selected examples of transcription for LightOnOCR-2-1B, LightOnOCR-2-1B-bbox and for reference our first version model LightOnOCR-1-1025.

Try it out with your own documents on our demo playground!

Key benchmarks

Transcription quality

Main results. LightOnOCR-2-1B scores 83.2 ± 0.9 on OlmOCR-Bench — the best among the systems we evaluated — while using only 1B parameters. The improvements are consistent across categories, with standout gains on ArXiv, old scans with math, and tables, driven by a cleaner/larger training mix, stronger scientific coverage, and higher-resolution training.

Capture d’écran 2026-01-19 à 16.15.41 Table 1: OlmOCR-Bench results (headers/footers category excluded). Per-column best is highlighted in blue and second best in bold. Results are taken from the corresponding published works; we additionally evaluate DeepSeekOCR and the Mistral OCR 3 API since they do not report OlmOCR-Bench numbers.

Speed

LightOnOCR is designed to fit into large-scale production document pipelines, where throughput is often just as important as accuracy. To capture that real-world constraint, we measure inference efficiency by running the entire OlmOCR-Bench evaluation end-to-end (1,403 pages) and report pages per second: the total number of pages divided by the wall-clock time needed to complete the benchmark.

Capture d’écran 2026-01-19 à 16.19.34 Table 2: Inference throughput on a single NVIDIA H100 (80GB).

What we’re releasing

LightOnOCR-2 is released as a small model family so you can pick the right tradeoff for your workflow instead of forcing everything into one checkpoint.

Default model: best OCR

LightOnOCR-2-1B is the OCR-only checkpoint and our default recommendation for most usages. If your job is “turn PDFs into clean text/Markdown reliably,” this is the one to use as it’s the strongest choice for transcription quality.

OCR + lightweight layout cues: bbox-capable variants

We’re also releasing bbox-capable checkpoints that can output bounding boxes for embedded figures/images (in addition to OCR). This is useful when you want lightweight localization (e.g., “extract text, and also tell me where the figures are”), without moving to a full document layout pipeline.

Because OCR and bbox objectives can pull the model in slightly different directions, we provide two options instead of overloading the default checkpoint:

Rather than overloading the default model, we provide:

  • LightOnOCR-2-1B-bbox: a bbox-focused checkpoint (best localization),
  • LightOnOCR-2-1B-bbox-soup: a merged tradeoff checkpoint (balanced OCR + bbox).

Base checkpoints (for fine-tuning / research)

Finally, we’re releasing two base checkpoints (one with bboxes, one without). These are meant for people who want to:

  • fine-tune on their own data/domains,
  • reproduce or extend our post-training steps (including RL recipes described in the preprint),
  • experiment with merges to build even stronger variants.

We provide here a recipe for finetuning using the models.

Transformers support: easier to run and fine-tune

LightOnOCR is now usable directly through the Hugging Face Transformers ecosystem (support has been merged upstream). Practically, that means:

  • you can run it with standard Transformers tooling (no requirement to start with vLLM),
  • fine-tuning is straightforward with common HF workflows (LoRA / PEFT / Trainer),
  • and CPU/local usage is feasible for lower-throughput settings (hardware-dependent, but much more accessible than “GPU-only pipelines”).

Watch this space for the preprint link!

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