Skip to main content
Qwen3-VL provides powerful document parsing capabilities that go beyond simple text extraction. The model can extract text while preserving layout information, positional data, and structured content in Qwen HTML format.

​
Capability Overview

The document parsing feature enables you to:
  • Extract text from complex document layouts
  • Preserve layout position information
  • Generate structured output in Qwen HTML format
  • Handle multi-column documents
  • Parse tables, forms, and structured content
  • Maintain reading order and document hierarchy

​
Example Usage

from transformers import AutoModelForImageTextToText, AutoProcessor

model = AutoModelForImageTextToText.from_pretrained(
    "Qwen/Qwen3-VL-235B-A22B-Instruct", dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-235B-A22B-Instruct")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "path/to/document.jpg",
            },
            {"type": "text", "text": "Parse this document and extract all text with layout information."},
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)
inputs = inputs.to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=2048)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

​
Try it Yourself

Explore the full document parsing cookbook with interactive examples: Open in Colab View on GitHub