OCR (Optical Character Recognition)

Extract text and structured content from images and PDF documents. Powered by Mistral OCR, this endpoint can understand complex document elements including tables, mathematical expressions, and multi-column layouts.

Endpoint

POST /proxy/v1/ocr

Request Body

Parameter	Type	Required	Description
document	object	Yes	The document to process (see Document Types below)
model	string	No	Model to use. Default: mistral-ocr-latest
id	string	No	Optional request identifier
pages	array<integer>	No	Specific pages to process (0-indexed). Example: [0, 1, 2]
include_image_base64	boolean	No	Include extracted images as base64 in response
image_limit	integer	No	Maximum number of images to extract
image_min_size	integer	No	Minimum height/width of images to extract
table_format	string	No	Table output format: "markdown" or "html"
extract_header	boolean	No	Extract document headers separately. Default: false
extract_footer	boolean	No	Extract document footers separately. Default: false
document_annotation_format	object	No	Response format for document annotation (see Response Formats)
bbox_annotation_format	object	No	Response format for bounding box annotation (see Response Formats)

Document Types

Image URL

{
  "document": {
    "type": "image_url",
    "image_url": "https://example.com/image.png"
  }
}

Document URL (PDF)

{
  "document": {
    "type": "document_url",
    "document_url": "https://example.com/document.pdf"
  }
}

File ID (previously uploaded)

{
  "document": {
    "type": "file",
    "file_id": "your_file_id_here"
  }
}

TIP

To process local files, first upload them to a publicly accessible URL (e.g., cloud storage) and use document_url or image_url.

Response Formats

Use these for document_annotation_format or bbox_annotation_format:

Text (default)

{ "type": "text" }

JSON Object

{ "type": "json_object" }

JSON Schema

{
  "type": "json_schema",
  "json_schema": { "your": "schema" }
}

Example Request

curl https://ai.hackclub.com/proxy/v1/ocr \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "document": {
      "type": "image_url",
      "image_url": "https://example.com/receipt.png"
    },
    "table_format": "markdown"
  }'

Example Response

{
  "model": "mistral-ocr-latest",
  "pages": [
    {
      "index": 0,
      "markdown": "# Invoice\n\nDate: 2024-01-15\n\n| Item | Quantity | Price |\n|------|----------|-------|\n| Widget | 5 | $10.00 |\n| Gadget | 2 | $25.00 |\n\n**Total: $100.00**",
      "images": [],
      "dimensions": {
        "width": 800,
        "height": 1200
      }
    }
  ],
  "document_annotation": null,
  "usage_info": {
    "pages_processed": 1,
    "doc_size_bytes": 102400
  }
}

Response Fields

Field	Type	Description
model	string	The model used for OCR
pages	array	List of OCR results per page
pages[].index	integer	Page index (0-based)
pages[].markdown	string	Extracted content in Markdown format
pages[].images	array	Extracted images with bounding boxes
pages[].dimensions	object	Page dimensions (width, height)
document_annotation	string|null	Formatted response if annotation format was specified
usage_info	object	Usage information for the request

Features

• Text Extraction: Preserves document structure including headers, paragraphs, and lists
• Table Recognition: Outputs tables in Markdown or HTML format
• Math Support: Handles mathematical expressions and LaTeX formatting
• Multi-language: Supports thousands of scripts and languages
• Image Extraction: Optionally extract embedded images with bounding boxes
• Structured Output: Use JSON schema for structured data extraction

Supported Formats

Images

• PNG, JPEG/JPG, AVIF, WebP, and more

Documents

• PDF, PPTX, DOCX, and more

Limitations

• Maximum file size: 50 MB
• Maximum pages: 1000 per request
• Character formatting (bold, italic, underline) is not preserved
• Footnotes and superscript text are preserved