Detect text in files (PDF/TIFF)

The Vision API can detect and transcribe text from PDF and TIFF files stored in Cloud Storage.

Document text detection from PDF and TIFF must be requested using the files:asyncBatchAnnotate function, which performs an offline (asynchronous) request and provides its status using the operations resources.

Output from a PDF/TIFF request is written to a JSON file created in the specified Cloud Storage bucket.

Limitations

The Vision API accepts PDF/TIFF files up to 2000 pages. Larger files will return an error.

Authentication

API keys are not supported for files:asyncBatchAnnotate requests. See Using a service account for instructions on authenticating with a service account.

The account used for authentication must have access to the Cloud Storage bucket that you specify for the output (roles/editor or roles/storage.objectCreator or above).

You can use an API key to query the status of the operation; see Using an API key for instructions.

Document text detection requests

Currently PDF/TIFF document detection is only available for files stored in Cloud Storage buckets. Response JSON files are similarly saved to a Cloud Storage bucket.

2010 US census PDF page
gs://cloud-samples-data/vision/pdf_tiff/census2010.pdf, Source: United States Census Bureau.

REST

Before using any of the request data, make the following replacements:

  • CLOUD_STORAGE_BUCKET: A Cloud Storage bucket/directory to save output files to, expressed in the following form:
    • gs://bucket/directory/
    The requesting user must have write permission to the bucket.
  • CLOUD_STORAGE_FILE_URI: the path to a valid file (PDF/TIFF) in a Cloud Storage bucket. You must at least have read privileges to the file. Example: 
    • gs://cloud-samples-data/vision/pdf_tiff/census2010.pdf
  • FEATURE_TYPE: A valid feature type. For files:asyncBatchAnnotate requests you can use the following feature types:
    • DOCUMENT_TEXT_DETECTION
    • TEXT_DETECTION
  • PROJECT_ID: Your Google Cloud project ID.

Field-specific considerations:

  • inputConfig - replaces the image field used in other Vision API requests. It contains two child fields:
    • gcsSource.uri - the Google Cloud Storage URI of the PDF or TIFF file (accessible to the user or service account making the request).
    • mimeType - one of the accepted file types: application/pdf or image/tiff.
  • outputConfig - specifies output details. It contains two child field:
    • gcsDestination.uri - a valid Google Cloud Storage URI. The bucket must be writeable by the user or service account making the request. The filename will be output-x-to-y, where x and y represent the PDF/TIFF page numbers included in that output file. If the file exists, its contents will be overwritten.
    • batchSize - specifies how many pages of output should be included in each output JSON file.

HTTP method and URL: 

POST https://vision.googleapis.com/v1/files:asyncBatchAnnotate

Request JSON body: 

{
  "requests":[
    {
      "inputConfig": {
        "gcsSource": {
          "uri": "CLOUD_STORAGE_FILE_URI"
        },
        "mimeType": "application/pdf"
      },
      "features": [
        {
          "type": "FEATURE_TYPE"
        }
      ],
      "outputConfig": {
        "gcsDestination": {
          "uri": "CLOUD_STORAGE_BUCKET"
        },
        "batchSize": 1
      }
    }
  ]
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: PROJECT_ID" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://vision.googleapis.com/v1/files:asyncBatchAnnotate"

PowerShell

Save the request body in a file named request.json, and execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "PROJECT_ID" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://vision.googleapis.com/v1/files:asyncBatchAnnotate" | Select-Object -Expand Content
Response:

A successful asyncBatchAnnotate request returns a response with a single name field:

{
  "name": "projects/usable-auth-library/operations/1efec2285bd442df"
}

This name represents a long-running operation with an associated ID (for example, 1efec2285bd442df), which can be queried using the v1.operations API.

To retrieve your Vision annotation response, send a GET request to the v1.operations endpoint, passing the operation ID in the URL:

GET https://vision.googleapis.com/v1/operations/operation-id

For example:

curl -X GET -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
-H "Content-Type: application/json" \
https://vision.googleapis.com/v1/projects/project-id/locations/location-id/operations/1efec2285bd442df

If the operation is in progress:

{
  "name": "operations/1efec2285bd442df",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.vision.v1.OperationMetadata",
    "state": "RUNNING",
    "createTime": "2019-05-15T21:10:08.401917049Z",
    "updateTime": "2019-05-15T21:10:33.700763554Z"
  }
}

Once the operation has completed, the state shows as DONE and your results are written to the Google Cloud Storage file you specified:

{
  "name": "operations/1efec2285bd442df",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.vision.v1.OperationMetadata",
    "state": "DONE",
    "createTime": "2019-05-15T20:56:30.622473785Z",
    "updateTime": "2019-05-15T20:56:41.666379749Z"
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.vision.v1.AsyncBatchAnnotateFilesResponse",
    "responses": [
      {
        "outputConfig": {
          "gcsDestination": {
            "uri": "gs://your-bucket-name/folder/"
          },
          "batchSize": 1
        }
      }
    ]
  }
}

The JSON in your output file is similar to that of an image's [document text detection request](/vision/docs/ocr), with the addition of a context field showing the location of the PDF or TIFF that was specified and the number of pages in the file:

output-1-to-1.json

Go

Before trying this sample, follow the Go setup instructions in the Vision quickstart using client libraries. For more information, see the Vision Go API reference documentation.

To authenticate to Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 


// detectAsyncDocumentURI performs Optical Character Recognition (OCR) on a
// PDF file stored in GCS.
func detectAsyncDocumentURI(w io.Writer, gcsSourceURI, gcsDestinationURI string) error {
	ctx := context.Background()

	client, err := vision.NewImageAnnotatorClient(ctx)
	if err != nil {
		return err
	}

	request := &visionpb.AsyncBatchAnnotateFilesRequest{
		Requests: []*visionpb.AsyncAnnotateFileRequest{
			{
				Features: []*visionpb.Feature{
					{
						Type: visionpb.Feature_DOCUMENT_TEXT_DETECTION,
					},
				},
				InputConfig: &visionpb.InputConfig{
					GcsSource: &visionpb.GcsSource{Uri: gcsSourceURI},
					// Supported MimeTypes are: "application/pdf" and "image/tiff".
					MimeType: "application/pdf",
				},
				OutputConfig: &visionpb.OutputConfig{
					GcsDestination: &visionpb.GcsDestination{Uri: gcsDestinationURI},
					// How many pages should be grouped into each json output file.
					BatchSize: 2,
				},
			},
		},
	}

	operation, err := client.AsyncBatchAnnotateFiles(ctx, request)
	if err != nil {
		return err
	}

	fmt.Fprintf(w, "Waiting for the operation to finish.")

	resp, err := operation.Wait(ctx)
	if err != nil {
		return err
	}

	fmt.Fprintf(w, "%v", resp)

	return nil
}

Java

Before trying this sample, follow the Java setup instructions in the Vision API Quickstart Using Client Libraries. For more information, see the Vision API Java reference documentation.

/**
 * Performs document text OCR with PDF/TIFF as source files on Google Cloud Storage.
 *
 * @param gcsSourcePath The path to the remote file on Google Cloud Storage to detect document
 *     text on.
 * @param gcsDestinationPath The path to the remote file on Google Cloud Storage to store the
 *     results on.
 * @throws Exception on errors while closing the client.
 */
public static void detectDocumentsGcs(String gcsSourcePath, String gcsDestinationPath)
    throws Exception {

  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // the "close" method on the client to safely clean up any remaining background resources.
  try (ImageAnnotatorClient client = ImageAnnotatorClient.create()) {
    List<AsyncAnnotateFileRequest> requests = new ArrayList<>();

    // Set the GCS source path for the remote file.
    GcsSource gcsSource = GcsSource.newBuilder().setUri(gcsSourcePath).build();

    // Create the configuration with the specified MIME (Multipurpose Internet Mail Extensions)
    // types
    InputConfig inputConfig =
        InputConfig.newBuilder()
            .setMimeType(
                "application/pdf") // Supported MimeTypes: "application/pdf", "image/tiff"
            .setGcsSource(gcsSource)
            .build();

    // Set the GCS destination path for where to save the results.
    GcsDestination gcsDestination =
        GcsDestination.newBuilder().setUri(gcsDestinationPath).build();

    // Create the configuration for the System.output with the batch size.
    // The batch size sets how many pages should be grouped into each json System.output file.
    OutputConfig outputConfig =
        OutputConfig.newBuilder().setBatchSize(2).setGcsDestination(gcsDestination).build();

    // Select the Feature required by the vision API
    Feature feature = Feature.newBuilder().setType(Feature.Type.DOCUMENT_TEXT_DETECTION).build();

    // Build the OCR request
    AsyncAnnotateFileRequest request =
        AsyncAnnotateFileRequest.newBuilder()
            .addFeatures(feature)
            .setInputConfig(inputConfig)
            .setOutputConfig(outputConfig)
            .build();

    requests.add(request);

    // Perform the OCR request
    OperationFuture<AsyncBatchAnnotateFilesResponse, OperationMetadata> response =
        client.asyncBatchAnnotateFilesAsync(requests);

    System.out.println("Waiting for the operation to finish.");

    // Wait for the request to finish. (The result is not used, since the API saves the result to
    // the specified location on GCS.)
    List<AsyncAnnotateFileResponse> result =
        response.get(180, TimeUnit.SECONDS).getResponsesList();

    // Once the request has completed and the System.output has been
    // written to GCS, we can list all the System.output files.
    Storage storage = StorageOptions.getDefaultInstance().getService();

    // Get the destination location from the gcsDestinationPath
    Pattern pattern = Pattern.compile("gs://([^/]+)/(.+)");
    Matcher matcher = pattern.matcher(gcsDestinationPath);

    if (matcher.find()) {
      String bucketName = matcher.group(1);
      String prefix = matcher.group(2);

      // Get the list of objects with the given prefix from the GCS bucket
      Bucket bucket = storage.get(bucketName);
      com.google.api.gax.paging.Page<Blob> pageList = bucket.list(BlobListOption.prefix(prefix));

      Blob firstOutputFile = null;

      // List objects with the given prefix.
      System.out.println("Output files:");
      for (Blob blob : pageList.iterateAll()) {
        System.out.println(blob.getName());

        // Process the first System.output file from GCS.
        // Since we specified batch size = 2, the first response contains
        // the first two pages of the input file.
        if (firstOutputFile == null) {
          firstOutputFile = blob;
        }
      }

      // Get the contents of the file and convert the JSON contents to an AnnotateFileResponse
      // object. If the Blob is small read all its content in one request
      // (Note: the file is a .json file)
      // Storage guide: https://cloud.google.com/storage/docs/downloading-objects
      String jsonContents = new String(firstOutputFile.getContent());
      Builder builder = AnnotateFileResponse.newBuilder();
      JsonFormat.parser().merge(jsonContents, builder);

      // Build the AnnotateFileResponse object
      AnnotateFileResponse annotateFileResponse = builder.build();

      // Parse through the object to get the actual response for the first page of the input file.
      AnnotateImageResponse annotateImageResponse = annotateFileResponse.getResponses(0);

      // Here we print the full text from the first page.
      // The response contains more information:
      // annotation/pages/blocks/paragraphs/words/symbols
      // including confidence score and bounding boxes
      System.out.format("%nText: %s%n", annotateImageResponse.getFullTextAnnotation().getText());
    } else {
      System.out.println("No MATCH");
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vision quickstart using client libraries. For more information, see the Vision Node.js API reference documentation.

To authenticate to Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 


// Imports the Google Cloud client libraries
const vision = require('@google-cloud/vision').v1;

// Creates a client
const client = new vision.ImageAnnotatorClient();

/**
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// Bucket where the file resides
// const bucketName = 'my-bucket';
// Path to PDF file within bucket
// const fileName = 'path/to/document.pdf';
// The folder to store the results
// const outputPrefix = 'results'

const gcsSourceUri = `gs://${bucketName}/${fileName}`;
const gcsDestinationUri = `gs://${bucketName}/${outputPrefix}/`;

const inputConfig = {
  // Supported mime_types are: 'application/pdf' and 'image/tiff'
  mimeType: 'application/pdf',
  gcsSource: {
    uri: gcsSourceUri,
  },
};
const outputConfig = {
  gcsDestination: {
    uri: gcsDestinationUri,
  },
};
const features = [{type: 'DOCUMENT_TEXT_DETECTION'}];
const request = {
  requests: [
    {
      inputConfig: inputConfig,
      features: features,
      outputConfig: outputConfig,
    },
  ],
};

const [operation] = await client.asyncBatchAnnotateFiles(request);
const [filesResponse] = await operation.promise();
const destinationUri =
  filesResponse.responses[0].outputConfig.gcsDestination.uri;
console.log('Json saved to: ' + destinationUri);

Python

Before trying this sample, follow the Python setup instructions in the Vision quickstart using client libraries. For more information, see the Vision Python API reference documentation.

To authenticate to Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

def async_detect_document(gcs_source_uri, gcs_destination_uri):
    """OCR with PDF/TIFF as source files on GCS"""
    import json
    import re
    from google.cloud import vision
    from google.cloud import storage

    # Supported mime_types are: 'application/pdf' and 'image/tiff'
    mime_type = "application/pdf"

    # How many pages should be grouped into each json output file.
    batch_size = 2

    client = vision.ImageAnnotatorClient()

    feature = vision.Feature(type_=vision.Feature.Type.DOCUMENT_TEXT_DETECTION)

    gcs_source = vision.GcsSource(uri=gcs_source_uri)
    input_config = vision.InputConfig(gcs_source=gcs_source, mime_type=mime_type)

    gcs_destination = vision.GcsDestination(uri=gcs_destination_uri)
    output_config = vision.OutputConfig(
        gcs_destination=gcs_destination, batch_size=batch_size
    )

    async_request = vision.AsyncAnnotateFileRequest(
        features=[feature], input_config=input_config, output_config=output_config
    )

    operation = client.async_batch_annotate_files(requests=[async_request])

    print("Waiting for the operation to finish.")
    operation.result(timeout=420)

    # Once the request has completed and the output has been
    # written to GCS, we can list all the output files.
    storage_client = storage.Client()

    match = re.match(r"gs://([^/]+)/(.+)", gcs_destination_uri)
    bucket_name = match.group(1)
    prefix = match.group(2)

    bucket = storage_client.get_bucket(bucket_name)

    # List objects with the given prefix, filtering out folders.
    blob_list = [
        blob
        for blob in list(bucket.list_blobs(prefix=prefix))
        if not blob.name.endswith("/")
    ]
    print("Output files:")
    for blob in blob_list:
        print(blob.name)

    # Process the first output file from GCS.
    # Since we specified batch_size=2, the first response contains
    # the first two pages of the input file.
    output = blob_list[0]

    json_string = output.download_as_bytes().decode("utf-8")
    response = json.loads(json_string)

    # The actual response for the first page of the input file.
    first_page_response = response["responses"][0]
    annotation = first_page_response["fullTextAnnotation"]

    # Here we print the full text from the first page.
    # The response contains more information:
    # annotation/pages/blocks/paragraphs/words/symbols
    # including confidence scores and bounding boxes
    print("Full text:\n")
    print(annotation["text"])

gcloud

The gcloud command you use depend on the file type.

  • To perform PDF text detection, use the gcloud ml vision detect-text-pdf command as shown in the following example:

    gcloud ml vision detect-text-pdf gs://my_bucket/input_file  gs://my_bucket/out_put_prefix

  • To perform TIFF text detection, use the gcloud ml vision detect-text-tiff command as shown in the following example:

    gcloud ml vision detect-text-tiff gs://my_bucket/input_file  gs://my_bucket/out_put_prefix

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the Vision reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Vision reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Vision reference documentation for Ruby.

Multi-regional support

You can now specify continent-level data storage and OCR processing. The following regions are currently supported:

  • us: USA country only
  • eu: The European Union

Locations

Cloud Vision offers you some control over where the resources for your project are stored and processed. In particular, you can configure Cloud Vision to store and process your data only in the European Union.

By default Cloud Vision stores and processes resources in a Global location, which means that Cloud Vision doesn't guarantee that your resources will remain within a particular location or region. If you choose the European Union location, Google will store your data and process it only in the European Union. You and your users can access the data from any location.

Setting the location using the API

The Vision API supports a global API endpoint (vision.googleapis.com) and also two region-based endpoints: a European Union endpoint (eu-vision.googleapis.com) and United States endpoint (us-vision.googleapis.com). Use these endpoints for region-specific processing. For example, to store and process your data in the European Union only, use the URI eu-vision.googleapis.com in place of vision.googleapis.com for your REST API calls:

  • https://eu-vision.googleapis.com/v1/projects/PROJECT_ID/locations/eu/images:annotate
  • https://eu-vision.googleapis.com/v1/projects/PROJECT_ID/locations/eu/images:asyncBatchAnnotate
  • https://eu-vision.googleapis.com/v1/projects/PROJECT_ID/locations/eu/files:annotate
  • https://eu-vision.googleapis.com/v1/projects/PROJECT_ID/locations/eu/files:asyncBatchAnnotate

To store and process your data in the United States only, use the US endpoint (us-vision.googleapis.com) with the preceding methods.

Setting the location using the client libraries

The Vision API client libraries accesses the global API endpoint (vision.googleapis.com) by default. To store and process your data in the European Union only, you need to explicitly set the endpoint (eu-vision.googleapis.com). The following code samples show how to configure this setting.

REST

Before using any of the request data, make the following replacements:

  • REGION_ID: One of the valid regional location identifiers:
    • us: USA country only
    • eu: The European Union
  • CLOUD_STORAGE_IMAGE_URI: the path to a valid image file in a Cloud Storage bucket. You must at least have read privileges to the file. Example: 
    • gs://cloud-samples-data/vision/pdf_tiff/census2010.pdf
  • CLOUD_STORAGE_BUCKET: A Cloud Storage bucket/directory to save output files to, expressed in the following form:
    • gs://bucket/directory/
    The requesting user must have write permission to the bucket.
  • FEATURE_TYPE: A valid feature type. For files:asyncBatchAnnotate requests you can use the following feature types:
    • DOCUMENT_TEXT_DETECTION
    • TEXT_DETECTION
  • PROJECT_ID: Your Google Cloud project ID.

Field-specific considerations:

  • inputConfig - replaces the image field used in other Vision API requests. It contains two child fields:
    • gcsSource.uri - the Google Cloud Storage URI of the PDF or TIFF file (accessible to the user or service account making the request).
    • mimeType - one of the accepted file types: application/pdf or image/tiff.
  • outputConfig - specifies output details. It contains two child field:
    • gcsDestination.uri - a valid Google Cloud Storage URI. The bucket must be writeable by the user or service account making the request. The filename will be output-x-to-y, where x and y represent the PDF/TIFF page numbers included in that output file. If the file exists, its contents will be overwritten.
    • batchSize - specifies how many pages of output should be included in each output JSON file.

HTTP method and URL: 

POST https://REGION_ID-vision.googleapis.com/v1/projects/PROJECT_ID/locations/REGION_ID/files:asyncBatchAnnotate

Request JSON body: 

{
  "requests":[
    {
      "inputConfig": {
        "gcsSource": {
          "uri": "CLOUD_STORAGE_IMAGE_URI"
        },
        "mimeType": "application/pdf"
      },
      "features": [
        {
          "type": "FEATURE_TYPE"
        }
      ],
      "outputConfig": {
        "gcsDestination": {
          "uri": "CLOUD_STORAGE_BUCKET"
        },
        "batchSize": 1
      }
    }
  ]
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: PROJECT_ID" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://REGION_ID-vision.googleapis.com/v1/projects/PROJECT_ID/locations/REGION_ID/files:asyncBatchAnnotate"

PowerShell

Save the request body in a file named request.json, and execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "PROJECT_ID" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://REGION_ID-vision.googleapis.com/v1/projects/PROJECT_ID/locations/REGION_ID/files:asyncBatchAnnotate" | Select-Object -Expand Content
Response:

A successful asyncBatchAnnotate request returns a response with a single name field:

{
  "name": "projects/usable-auth-library/operations/1efec2285bd442df"
}

This name represents a long-running operation with an associated ID (for example, 1efec2285bd442df), which can be queried using the v1.operations API.

To retrieve your Vision annotation response, send a GET request to the v1.operations endpoint, passing the operation ID in the URL:

GET https://vision.googleapis.com/v1/operations/operation-id

For example:

curl -X GET -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
-H "Content-Type: application/json" \
https://vision.googleapis.com/v1/projects/project-id/locations/location-id/operations/1efec2285bd442df

If the operation is in progress:

{
  "name": "operations/1efec2285bd442df",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.vision.v1.OperationMetadata",
    "state": "RUNNING",
    "createTime": "2019-05-15T21:10:08.401917049Z",
    "updateTime": "2019-05-15T21:10:33.700763554Z"
  }
}

Once the operation has completed, the state shows as DONE and your results are written to the Google Cloud Storage file you specified:

{
  "name": "operations/1efec2285bd442df",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.vision.v1.OperationMetadata",
    "state": "DONE",
    "createTime": "2019-05-15T20:56:30.622473785Z",
    "updateTime": "2019-05-15T20:56:41.666379749Z"
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.vision.v1.AsyncBatchAnnotateFilesResponse",
    "responses": [
      {
        "outputConfig": {
          "gcsDestination": {
            "uri": "gs://your-bucket-name/folder/"
          },
          "batchSize": 1
        }
      }
    ]
  }
}

The JSON in your output file is similar to that of an image's document text detection response if you used the DOCUMENT_TEXT_DETECTION feature, or text detection response if you used the TEXT_DETECTION feature. The output will have an additional context field showing the location of the PDF or TIFF that was specified and the number of pages in the file:

output-1-to-1.json

Go

Before trying this sample, follow the Go setup instructions in the Vision quickstart using client libraries. For more information, see the Vision Go API reference documentation.

To authenticate to Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

import (
	"context"
	"fmt"

	vision "cloud.google.com/go/vision/apiv1"
	"google.golang.org/api/option"
)

// setEndpoint changes your endpoint.
func setEndpoint(endpoint string) error {
	// endpoint := "eu-vision.googleapis.com:443"

	ctx := context.Background()
	client, err := vision.NewImageAnnotatorClient(ctx, option.WithEndpoint(endpoint))
	if err != nil {
		return fmt.Errorf("NewImageAnnotatorClient: %w", err)
	}
	defer client.Close()

	return nil
}

Java

Before trying this sample, follow the Java setup instructions in the Vision API Quickstart Using Client Libraries. For more information, see the Vision API Java reference documentation.

ImageAnnotatorSettings settings =
    ImageAnnotatorSettings.newBuilder().setEndpoint("eu-vision.googleapis.com:443").build();

// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
ImageAnnotatorClient client = ImageAnnotatorClient.create(settings);

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vision quickstart using client libraries. For more information, see the Vision Node.js API reference documentation.

To authenticate to Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

// Imports the Google Cloud client library
const vision = require('@google-cloud/vision');

async function setEndpoint() {
  // Specifies the location of the api endpoint
  const clientOptions = {apiEndpoint: 'eu-vision.googleapis.com'};

  // Creates a client
  const client = new vision.ImageAnnotatorClient(clientOptions);

  // Performs text detection on the image file
  const [result] = await client.textDetection('./resources/wakeupcat.jpg');
  const labels = result.textAnnotations;
  console.log('Text:');
  labels.forEach(label => console.log(label.description));
}
setEndpoint();

Python

Before trying this sample, follow the Python setup instructions in the Vision quickstart using client libraries. For more information, see the Vision Python API reference documentation.

To authenticate to Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

from google.cloud import vision

client_options = {"api_endpoint": "eu-vision.googleapis.com"}

client = vision.ImageAnnotatorClient(client_options=client_options)

Try it for yourself

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