> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gp.scale.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction to IRIS

**IRIS is Scale's OCR capability that transforms document images and PDFs into structured text.** It provides a comprehensive "layout-then-OCR" pipeline that combines **layout detection, specialized OCR models, and intelligent assembly** to extract meaningful information from complex documents.

***

## When to Use IRIS?

IRIS is designed for teams that need:

* **Custom OCR pipeline control:** Fine-tune layout detection, OCR models, and assembly for specialized document types
* **Maximum accuracy optimization:** Experiment with 15+ OCR models to find the best fit for your specific documents
* **Minimum latency requirements:** Optimize processing speed for your specific use case through custom configuration
* **Specialized model development:** Build and integrate custom OCR models or layout detectors for unique document challenges
* **Multi-language experimentation:** Test different OCR engines for non-Germanic languages to find optimal accuracy

### For Standard Production Use

If you need stable batch document processing without custom requirements, **use Reducto within Dex**:

* **Production stability** with proven reliability
* **Good for batch processing** with somewhat high but acceptable latency
* **Auto-scaling** with your workload
* **Best accuracy** for English and Germanic languages
* **Lower operational complexity**

See the **[When to choose Iris?](/docs/capabilities/ocr/when-to-choose-iris)** guide for a detailed comparison.

***

## IRIS Capabilities

IRIS provides advanced capabilities for teams with specialized OCR needs:

* **Layout-Aware Processing:** Automatically detects and classifies different regions (text, tables, images, etc.) before applying specialized OCR
* **15+ OCR Models:** Choose from Tesseract, EasyOCR, PaddleOCR, Surya, GPT-4o, Gemini, and specialized models for Arabic and other languages
* **Table-Specific Processing:** Apply specialized OCR models optimized for tabular data extraction
* **Extensible Architecture:** Easily add custom OCR models or layout detectors through simple adapter interfaces
* **Multi-Language Flexibility:** Test and optimize for documents in multiple languages including Arabic with specialized support
* **Complete Pipeline Control:** Inspect and configure each step—layout detection, content extraction, and assembly

***

## How IRIS Works

IRIS follows a three-stage pipeline:

### 1. Layout Detection

Detects and classifies regions in document images with bounding boxes and content types:

* **Text regions** for paragraphs and body content
* **Table regions** for structured tabular data
* **Image regions** for figures and diagrams
* **Other content types** as needed

Supported layout detection models:

* **rt\_detr\_bce:** RT-DETR model (best performance)
* **surya\_bce:** Surya layout detection
* **yolo\_bce:** YOLO-based detection

### 2. OCR Processing

Applies specialized OCR models to each detected region based on content type:

* **Text OCR:** Optimized for continuous text (paragraphs, headings, etc.)
* **Table OCR:** Specialized models for accurate table extraction
* **Image Handling:** Preserves image crops for inclusion in output

Available OCR engines include:

* **Open-source models:** EasyOCR, Tesseract, PaddleOCR, Surya
* **Vision-language models:** GPT-4o, Gemini (via LiteLLM), Qwen2-VL, Qwen2.5-VL
* **Specialized models:** Arabic Nougat (small/base/large), QAARI, MBZUAI-AIN
* **Cloud services:** Azure Computer Vision
* **Advanced models:** SmolDocling

### 3. Assembly

Combines extracted content back into useful formats:

* **MarkdownAssembler:** Converts to structured markdown (default)
* **JSONAssembler:** Outputs detailed JSON for debugging and inspection

***

## Core Features

### Modular Architecture

Each pipeline component (layout detector, OCR model, assembler) is independently configurable and extensible. Add custom models by implementing simple adapter interfaces.

### Inspection and Debugging

Save intermediate results at each pipeline stage to understand and optimize the processing:

* Layout detection bounding boxes and classifications
* Individual OCR outputs per region
* Final assembled output

### Configuration Management

Use command-line arguments or YAML configuration files for reproducible processing:

```yaml theme={null}
layout: rt_detr_bce
text_ocr: easyocr
table_ocr: gemini
output_dir: results
save_intermediate: true
```

### Performance Optimization

First-time model usage downloads weights; subsequent runs use cached models for faster processing.

***

## Common Use Cases

* **Document Digitization:** Convert scanned documents and PDFs to searchable text
* **Form Processing:** Extract structured data from forms with mixed content types
* **Table Extraction:** Accurately capture tabular data from complex documents
* **Multi-Language Documents:** Process documents in various languages including Arabic
* **Research and Analysis:** Extract text and data from academic papers, reports, and technical documents
* **Archive Processing:** Batch process large document collections

***

## Getting Started

IRIS is integrated with Dex for seamless document processing:

### Python Package

Use programmatically through the Dex SDK:

```python theme={null}
from dex_sdk.client import DexClient
from dex_core.models.parse_job import IrisParseEngineOptions, IrisParseJobParams

# Initialize client and project
dex_client = DexClient(base_url="your-dex-url")
project = await dex_client.create_project(name="ocr-project")

# Upload and parse document
dex_file = await project.upload_file("document.pdf")
parse_job = await dex_file.start_parse_job(
    IrisParseJobParams(options=IrisParseEngineOptions())
)
result = await parse_job.get_result()
```

***

## Language Support

IRIS supports multi-language OCR with models optimized for different language families. Specialized support includes:

* **Arabic:** Arabic Nougat models, QAARI, MBZUAI-AIN, Gemini
* **Multi-language:** EasyOCR, Tesseract, PaddleOCR with broad language coverage
* **Universal:** Vision-language models (GPT-4o, Gemini, Qwen) supporting many languages

***

## Key Advantages

### Extensibility

Designed for easy extension. Add support for new OCR libraries, layout detectors, or assembly formats through simple adapter classes.

### Transparency

Inspect every pipeline stage to understand model behavior, debug issues, and optimize results for your specific documents.

### Flexibility

Mix and match components—use different OCR models for text vs. tables, choose layout detectors based on document type, and customize assembly output.

### API Integration

Vision-language models (Gemini, GPT-4o) use LiteLLM for unified API access, making it easy to leverage advanced AI capabilities.

***

## Next Steps

To start using IRIS:

1. Review the [Getting Started with IRIS](/docs/capabilities/ocr/getting-started-with-iris) guide for step-by-step instructions
2. Learn about IRIS's multi-stage OCR pipeline and configuration options
3. Integrate IRIS into your Dex-based document processing workflows

IRIS provides the building blocks for robust document OCR while giving you complete control over the processing pipeline.