> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/QwenLM/Qwen3-VL/llms.txt
> Use this file to discover all available pages before exploring further.

# OCR & Key Information Extraction

> General OCR with 32 language support and key information extraction

# OCR & Key Information Extraction

Qwen3-VL provides powerful optical character recognition (OCR) capabilities with expanded language support and robust performance across diverse conditions. The model excels at both general text recognition and targeted key information extraction.

## Capabilities

### Expanded Language Support

Qwen3-VL supports OCR in **32 languages**, significantly expanded from the previous 10:

* Latin-script languages
* Chinese (Simplified and Traditional)
* Japanese, Korean
* Arabic, Hebrew, and other RTL scripts
* Cyrillic scripts
* Indic languages
* And many more

### Robust Text Recognition

The model performs reliably even in challenging conditions:

* **Low Light**: Extract text from dimly lit or dark images
* **Blur**: Handle motion blur and out-of-focus text
* **Tilt & Rotation**: Process text at various angles
* **Rare Characters**: Recognize ancient characters and specialized symbols
* **Technical Jargon**: Handle domain-specific terminology and notation

## Key Information Extraction

Beyond general OCR, Qwen3-VL can extract specific information from documents:

* **Forms**: Extract field values from structured forms
* **Receipts & Invoices**: Parse transaction details, dates, amounts
* **Business Cards**: Extract contact information
* **IDs & Documents**: Pull specific fields from identification documents
* **Tables**: Extract data from tabular formats

## Use Cases

* **Document Digitization**: Convert physical documents to searchable text
* **Data Entry Automation**: Eliminate manual typing from forms and receipts
* **Multilingual Content**: Process documents in various languages
* **Accessibility**: Make visual content accessible to screen readers
* **Search & Indexing**: Enable full-text search on image-based documents

## Try It Out

Explore OCR and key information extraction with our interactive cookbook:

> **[OCR Cookbook](https://github.com/QwenLM/Qwen3-VL/blob/main/cookbooks/ocr.ipynb)**
>
> Stronger text recognition capabilities in natural scenes and multiple languages, supporting diverse key information extraction needs.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QwenLM/Qwen3-VL/blob/main/cookbooks/ocr.ipynb)

## Advanced Features

* **Natural Scene Text**: Extract text from photos, signs, and real-world images
* **Handwriting Recognition**: Process handwritten notes and annotations
* **Mixed Content**: Handle documents with multiple languages and scripts
* **Layout Understanding**: Maintain reading order in complex layouts

## Performance Highlights

* 32 language support (up from 10)
* Robust in challenging conditions
* Accurate recognition of rare and ancient characters
* Improved long-document structure parsing

## Related Capabilities

* [Document Parsing](https://mintlify.com/QwenLM/Qwen3-VL/capabilities/document-parsing.md) - Full document structure extraction
* [2D Grounding](https://mintlify.com/QwenLM/Qwen3-VL/capabilities/grounding-2d.md) - Locate text within images
* [Video Understanding](https://mintlify.com/QwenLM/Qwen3-VL/capabilities/video-understanding.md) - OCR in video content


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