# Recognize text in documents (OCR)

Robot: `/document/ocr`

🤖/document/ocr recognizes text in documents and returns it in a machine-readable format.

With this <dfn>Robot</dfn>, you can detect and extract text from PDFs using optical character recognition (OCR).

For example, you can use the results to obtain the content of invoices, legal documents or restaurant menus. You can also pass the text down to other <dfn>Robots</dfn> to filter documents that contain (or do not contain) certain phrases.

Stage: ga

## Usage example

Recognize text in an uploaded document and save it to a JSON file:

```json
{
  "steps": {
    "recognized": {
      "robot": "/document/ocr",
      "use": ":original",
      "provider": "gcp"
    }
  }
}
```

## Parameters

* `interpolate`: Controls whether Assembly Variables are interpolated for individual instruction fields.

  By default, most Robot instruction fields interpolate Assembly Variables. Set this to `false` to treat every instruction field as literal text, or set an individual field path to `false` to treat only that field as literal text. For Robot-specific fields that are literal by default, set this to `true` or set that field path to `true` to opt back into interpolation.

  Use field names such as `path`, or dotted paths such as `ffmpeg.vf` for nested objects.

* `output_meta`: Allows you to specify a set of metadata that is more expensive on CPU power to calculate, and thus is disabled by default to keep your Assemblies processing fast.

  For images, you can add `"has_transparency": true` in this object to extract if the image contains transparent parts and `"dominant_colors": true` to extract an array of hexadecimal color codes from the image.

  For images, you can also add `"blurhash": true` to extract a [BlurHash](https://blurha.sh) string — a compact representation of a placeholder for the image, useful for showing a blurred preview while the full image loads.

  For videos, you can add the `"colorspace": true` parameter to extract the colorspace of the output video.

  For videos, you can also add `"interlaced": true` to detect whether the video is interlaced. This combines the cheap ffprobe `field_order` flag with a bounded `idet` sampling pass over the first frames of the source, exposing `interlaced`, `field_order`, and a diagnostic `interlace_detection` object under `file.meta`. This is computationally expensive and billed accordingly.

  For audio, you can add `"mean_volume": true` to get a single value representing the mean average volume of the audio file.

  You can also set this to `false` to skip metadata extraction and speed up transcoding.

* `user_meta`: Adds custom metadata to each file emitted by this Robot without modifying the file’s contents.

  The values are merged with any existing `user_meta` carried by the input file. If both objects contain the same key, this Robot’s value takes precedence. Assembly Variables are supported, for example `{ "internal_file_id": "${file.id}" }`.

* `result`: Whether the results of this Step should be present in the Assembly Status JSON

* `queue`: Setting the queue to 'batch', manually downgrades the priority of jobs for this step to avoid consuming Priority job slots for jobs that don't need zero queue waiting times

* `force_accept`: Force a Robot to accept a file type it would have ignored.

  By default, Robots ignore files they are not familiar with.
  [🤖/video/encode](/docs/robots/video-encode.md), for
  example, will happily ignore input images.

  With the `force_accept` parameter set to `true`, you can force Robots to accept all files thrown at them.
  This will typically lead to errors and should only be used for debugging or combatting edge cases.

* `ignore_errors`: Ignore errors during specific phases of processing.

  Setting this to `["meta"]` will cause the Robot to ignore errors during metadata extraction.

  Setting this to `["execute"]` will cause the Robot to ignore errors during the main execution phase.

  Setting this to `true` is equivalent to `["meta", "execute"]` and will ignore errors in both phases.

* `use`: Specifies which Step(s) to use as input.

  * You can pick any names for Steps except `":original"` (reserved for user uploads handled by Transloadit)
  * You can provide several Steps as input with arrays:
    ```json
    {
      "use": [
        ":original",
        "encoded",
        "resized"
      ]
    }
    ```
  * You can also tag input Steps with `as` to pass semantic intent to robots:
    ```json
    {
      "use": [
        {
          "name": ":original",
          "as": "image"
        },
        {
          "name": ":original",
          "as": "mask"
        }
      ]
    }
    ```

  > [!Tip]
  > That's likely all you need to know about `use`, but you can view [Advanced use cases](/docs/topics/use-parameter.md).

* `provider`: Chooses the best provider based on your request.

  Set this to `"aws"` or `"gcp"` to force a specific provider.

  AWS supports detection for the following languages: English, Arabic, Russian, German, French, Italian, Portuguese and Spanish. GCP allows for a wider range of languages, with varying levels of support which can be found on the [official documentation](https://cloud.google.com/vision/docs/languages/).

* `granularity`: Whether to return a full response including coordinates for the text (`"full"`), or a flat list of the extracted phrases (`"list"`). This parameter has no effect if the `format` parameter is set to `"text"`.

* `format`: In what format to return the extracted text.

  * `"json"` returns a JSON file.
  * `"meta"` does not return a file, but stores the data inside Transloadit's file object (under `${file.meta.recognized_text}`, which is an array of strings) that's passed around between encoding <dfn>Steps</dfn>, so that you can use the values to burn the data into videos, filter on them, etc.
  * `"text"` returns the recognized text as a plain UTF-8 encoded text file.