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

# Optical Character Recognition (OCR)

> Extract text from images

Optical Character Recognition (OCR) is a technology that converts different types of documents,
such as scanned paper documents, PDF files, or images, into editable and searchable data.

## OCR Strategy

Chunkr AI API always returns OCR results. You can configure the OCR strategy using the `ocr_strategy` parameter.

We have two strategies:

* `All` (Default): Processes all pages with our OCR model.
* `Auto`: Intelligently applies OCR only to pages with missing or low-quality text. When a text layer is present, the bounding boxes from that layer are used instead of running OCR.

<CodeGroup>
  ```python Python theme={"system"}
  from chunkr_ai import Chunkr
  from chunkr_ai.models import Configuration, OcrStrategy

  chunkr = Chunkr()

  chunkr.upload("path/to/file", Configuration(
      ocr_strategy=OcrStrategy.AUTO # can also be OcrStrategy.ALL
  ))
  ```

  ```bash cURL theme={"system"}
  curl --request POST \
    --url https://api.chunkr.ai/api/v1/task/parse \
    --header 'Authorization: YOUR_API_KEY' \
    --header 'Content-Type: application/json' \
    --data '{
      "file": "base64_encoded_file_content",
      "file_name": "document.pdf",
      "ocr_strategy": "Auto"
    }'
  ```
</CodeGroup>

The `Auto` strategy provides the best balance between accuracy and performance for most use cases.
Use the `All` strategy when you need to ensure consistent text extraction across all pages or when you suspect the existing text layer might be unreliable.

## OCR + Layout Analysis

OCR and Layout Analysis together are a powerful combination.
It allows us to get word level bounding boxes and text while also understanding the layout of the document.

You can use that to make experiences like:

* Highlighting exact numbers in a table
* Highlighting text in images
* Embedding the text from pictures for semantic search

## Other common use cases

* Digitizing old books and documents
* Processing invoices and receipts
* Automating form data entry
* Reading license plates
* Converting handwritten notes to digital text
* Extracting text from screenshots and images