---
license: apache-2.0
language:
- ja
- en
base_model:
- Qwen/Qwen3.5-2B
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- ocr
- document-ai
- vision-language
- qwen3_5
- multimodal
- japanese
---

# Qwen3.5-OCR-JP-2B

**Qwen3.5-OCR-JP-2B** is a Japanese/English Vision-Language OCR model built on top of Qwen3.5-2B. Output schema is compatible with [Chandra OCR 2 (datalab-to/chandra)](https://github.com/datalab-to/chandra) — HTML layout blocks with bounding boxes and labels.

## Focus

Training data emphasizes the following Japanese document features:

- Ruby annotations — emitted as HTML5 ruby markup, e.g. `<ruby>漢字<rt>かんじ</rt></ruby>`
- Japanese handwriting, vertical writing

## Quickstart

### vLLM (recommended)

```python
import base64, io
from PIL import Image
from vllm import LLM, SamplingParams

PROMPT = "OCR this image as HTML layout blocks with bbox and label."

llm = LLM(
    model="ebinan92/Qwen3.5-ocr-jp-2b",
    dtype="bfloat16",
    max_model_len=12288,
    limit_mm_per_prompt={"image": 1},
    trust_remote_code=True,
)
sampling = SamplingParams(temperature=0.0, top_p=0.1, max_tokens=8000)

image = Image.open("page.png").convert("RGB")
buf = io.BytesIO()
image.save(buf, format="PNG")
b64 = base64.b64encode(buf.getvalue()).decode()

messages = [{
    "role": "user",
    "content": [
        {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}},
        {"type": "text", "text": PROMPT},
    ],
}]
print(llm.chat(messages, sampling_params=sampling)[0].outputs[0].text)
```

Requires `vllm>=0.19.1` and `transformers>=5.5.1`.

### transformers

```python
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForImageTextToText

PROMPT = "OCR this image as HTML layout blocks with bbox and label."

ckpt = "ebinan92/Qwen3.5-ocr-jp-2b"
processor = AutoProcessor.from_pretrained(ckpt)
model = AutoModelForImageTextToText.from_pretrained(
    ckpt, dtype=torch.bfloat16, device_map="auto"
)

image = Image.open("page.png").convert("RGB")
messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": image},
        {"type": "text", "text": PROMPT},
    ],
}]
inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

out = model.generate(**inputs, max_new_tokens=8000, do_sample=False)
print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])
```

## Benchmarks

| Benchmark | Metric | chandra-ocr-2 | Qwen3.5-ocr-jp-2b | sarashina2.2-ocr |
|---|---|---|---|---|
| [olmOCR-bench](https://huggingface.co/datasets/allenai/olmOCR-bench) | Accuracy ↑ | **85.9**<sup>†</sup> | 82.8 | — |
| [VJRODa](https://gitlab.llm-jp.nii.ac.jp/datasets/vjroda)<sup>※</sup> | CER % ↓ | **7.2** | 7.3 | 12.0 |
| [VJRODa](https://gitlab.llm-jp.nii.ac.jp/datasets/vjroda)<sup>※</sup> | BLEU ↑ | 94.2 | **94.6** | 91.4 |
| [JaWildText](https://huggingface.co/datasets/llm-jp/jawildtext) | CER % ↓ | 7.68 | **6.33** | 47.78 |

sarashina2.2-ocr's olmOCR-bench overall is omitted because its [HF card](https://huggingface.co/sbintuitions/sarashina2.2-ocr) does not report the `baseline` row.

<sup>※</sup> VJRODa is evaluated on 92 / 100 samples (8 PDFs are NDL WARP-restricted and unavailable).  
<sup>†</sup> olmOCR-bench score for chandra-ocr-2 is taken from the official [HF card](https://huggingface.co/datalab-to/chandra-ocr-2).

<details>
<summary>olmOCR-bench JSONL breakdown</summary>

| JSONL | chandra-ocr-2<sup>†</sup> | Qwen3.5-ocr-jp-2b |
|---|---|---|
| arxiv_math | **90.2** | 85.7 |
| table_tests | **89.9** | 88.1 |
| baseline | **99.6** | 99.1 |
| headers_footers | **92.5** | 90.3 |
| old_scans_math | **89.3** | 81.9 |
| long_tiny_text | 92.1 | **92.3** |
| multi_column | **83.5** | 79.6 |
| old_scans | **49.8** | 45.4 |

</details>

## Limitations

- Works only with the single fixed prompt above. It is not tuned for other tasks or free-form instructions.
- Trained primarily on Japanese and English. Coverage of other languages (Chinese, Korean, etc.) is incidental.

## License

Apache 2.0.

This model is derived from Qwen3.5-2B, trained on independently constructed datasets. No outputs or weights from `datalab-to/chandra-ocr-2` (or any other Chandra release) were used.

## Acknowledgements

- [Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B) — base model (Apache 2.0)
- [Chandra](https://github.com/datalab-to/chandra) — format reference