# 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.

### [​](https://docs.chunkr.ai/docs/features/ocr#ocr-strategy)OCR Strategy <a href="#ocr-strategy" id="ocr-strategy"></a>

Fractur 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.

PythoncURL

```python
from chunkr_ai import Chunkr
from chunkr_ai.models import Configuration, OcrStrategy

chunkr = Chunkr()

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

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.

### [​](https://docs.chunkr.ai/docs/features/ocr#ocr-layout-analysis)OCR + Layout Analysis <a href="#ocr-layout-analysis" id="ocr-layout-analysis"></a>

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

### [​](https://docs.chunkr.ai/docs/features/ocr#other-common-use-cases)Other common use cases <a href="#other-common-use-cases" id="other-common-use-cases"></a>

* 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


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