Detect handwriting in images

Handwriting detection with Optical Character Recognition (OCR)

The Vision API can detect and extract text from images:

  • DOCUMENT_TEXT_DETECTION extracts text from an image (or file); the response is optimized for dense text and documents. The JSON includes page, block, paragraph, word, and break information.

    Screenshot simulating how an OCR system might identify and extract text, highlighting headings, paragraphs, and icons.

    One specific use of DOCUMENT_TEXT_DETECTION is to detect handwriting in an image.

    Lined paper with Google Cloud Platform written in cursive.

Try it for yourself

If you're new to Google Cloud, create an account to evaluate how Cloud Vision API performs in real-world scenarios. New customers also get 300ドル in free credits to run, test, and deploy workloads.

Try Cloud Vision API free

Document text detection requests

Set up your Google Cloud project and authentication

If you have not created a Google Cloud project, do so now. Expand this section for instructions.

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get 300ドル in free credits to run, test, and deploy workloads.
  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

    Go to project selector

  3. Verify that billing is enabled for your Google Cloud project.

  4. Enable the Vision API.

    Roles required to enable APIs

    To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

    Enable the API

  5. Install the Google Cloud CLI.

  6. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

  7. To initialize the gcloud CLI, run the following command:

    gcloudinit
  8. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

    Go to project selector

  9. Verify that billing is enabled for your Google Cloud project.

  10. Enable the Vision API.

    Roles required to enable APIs

    To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

    Enable the API

  11. Install the Google Cloud CLI.

  12. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

  13. To initialize the gcloud CLI, run the following command:

    gcloudinit

Detect document text in a local image

You can use the Vision API to perform feature detection on a local image file.

For REST requests, send the contents of the image file as a base64 encoded string in the body of your request.

For gcloud and client library requests, specify the path to a local image in your request.

REST

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

  • BASE64_ENCODED_IMAGE: The base64 representation (ASCII string) of your binary image data. This string should look similar to the following string:
    • /9j/4QAYRXhpZgAA...9tAVx/zDQDlGxn//2Q==
    Visit the base64 encode topic for more information.
  • PROJECT_ID: Your Google Cloud project ID.

HTTP method and URL:

POST https://vision.googleapis.com/v1/images:annotate

Request JSON body:

{
 "requests": [
 {
 "image": {
 "content": "BASE64_ENCODED_IMAGE"
 },
 "features": [
 {
 "type": "DOCUMENT_TEXT_DETECTION"
 }
 ]
 }
 ]
}

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/images:annotate"

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/images:annotate" | Select-Object -Expand Content

If the request is successful, the server returns a 200 OK HTTP status code and the response in JSON format.

Response

{
 "responses": [
 {
 "textAnnotations": [
 {
 "locale": "en",
 "description": "O Google Cloud Platform\n",
 "boundingPoly": {
 "vertices": [
 {
 "x": 14,
 "y": 11
 },
 {
 "x": 279,
 "y": 11
 },
 {
 "x": 279,
 "y": 37
 },
 {
 "x": 14,
 "y": 37
 }
 ]
 }
 },
 ],
 "fullTextAnnotation": {
 "pages": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ]
 },
 "width": 281,
 "height": 44,
 "blocks": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ]
 },
 "boundingBox": {
 "vertices": [
 {
 "x": 14, "y": 11
 },
 {
 "x": 279, "y": 11
 },
 {
 "x": 279, "y": 37
 },
 {
 "x": 14, "y": 37
 }
 ]
 },
 "paragraphs": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ]
 },
 "boundingBox": {
 "vertices": [
 {
 "x": 14, "y": 11
 },
 {
 "x": 279, "y": 11
 },
 {
 "x": 279, "y": 37
 },
 {
 "x": 14, "y": 37
 }
 ]
 },
 "words": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ]
 },
 "boundingBox": {
 "vertices": [
 {
 "x": 14, "y": 11
 },
 {
 "x": 23, "y": 11
 },
 {
 "x": 23, "y": 37
 },
 {
 "x": 14, "y": 37
 }
 ]
 },
 "symbols": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ],
 "detectedBreak": {
 "type": "SPACE"
 }
 },
 "boundingBox": {
 "vertices": [
 {
 "x": 14, "y": 11
 },
 {
 "x": 23, "y": 11
 },
 {
 "x": 23, "y": 37
 },
 {
 "x": 14, "y": 37
 }
 ]
 },
 "text": "O"
 }
 ]
 },
 ]
 }
 ],
 "blockType": "TEXT"
 }
 ]
 }
 ],
 "text": "Google Cloud Platform\n"
 }
 }
 ]
}

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.


// detectDocumentText gets the full document text from the Vision API for an image at the given file path.
funcdetectDocumentText(wio.Writer,filestring)error{
ctx:=context.Background()
client,err:=vision.NewImageAnnotatorClient(ctx)
iferr!=nil{
returnerr
}
f,err:=os.Open(file)
iferr!=nil{
returnerr
}
deferf.Close()
image,err:=vision.NewImageFromReader(f)
iferr!=nil{
returnerr
}
annotation,err:=client.DetectDocumentText(ctx,image,nil)
iferr!=nil{
returnerr
}
ifannotation==nil{
fmt.Fprintln(w,"No text found.")
}else{
fmt.Fprintln(w,"Document Text:")
fmt.Fprintf(w,"%q\n",annotation.Text)
fmt.Fprintln(w,"Pages:")
for_,page:=rangeannotation.Pages{
fmt.Fprintf(w,"\tConfidence: %f, Width: %d, Height: %d\n",page.Confidence,page.Width,page.Height)
fmt.Fprintln(w,"\tBlocks:")
for_,block:=rangepage.Blocks{
fmt.Fprintf(w,"\t\tConfidence: %f, Block type: %v\n",block.Confidence,block.BlockType)
fmt.Fprintln(w,"\t\tParagraphs:")
for_,paragraph:=rangeblock.Paragraphs{
fmt.Fprintf(w,"\t\t\tConfidence: %f",paragraph.Confidence)
fmt.Fprintln(w,"\t\t\tWords:")
for_,word:=rangeparagraph.Words{
symbols:=make([]string,len(word.Symbols))
fori,s:=rangeword.Symbols{
symbols[i]=s.Text
}
wordText:=strings.Join(symbols,"")
fmt.Fprintf(w,"\t\t\t\tConfidence: %f, Symbols: %s\n",word.Confidence,wordText)
}
}
}
}
}
returnnil
}

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.

publicstaticvoiddetectDocumentText(StringfilePath)throwsIOException{
List<AnnotateImageRequest>requests=newArrayList<>();
ByteStringimgBytes=ByteString.readFrom(newFileInputStream(filePath));
Imageimg=Image.newBuilder().setContent(imgBytes).build();
Featurefeat=Feature.newBuilder().setType(Type.DOCUMENT_TEXT_DETECTION).build();
AnnotateImageRequestrequest=
AnnotateImageRequest.newBuilder().addFeatures(feat).setImage(img).build();
requests.add(request);
// 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(ImageAnnotatorClientclient=ImageAnnotatorClient.create()){
BatchAnnotateImagesResponseresponse=client.batchAnnotateImages(requests)<;
ListAnnotateIma>geResponseresponses=response.getResponsesList();
client.close();
for(AnnotateImageResponseres:responses){
if(res.hasError()){
System.out.format("Error: %s%n",res.getError().getMessage());
return;
}
// For full list of available annotations, see http://g.co/cloud/vision/docs
TextAnnotationannotation=res.getFullTextAnnotation();
for(Pagepage:annotation.getPagesList()){
StringpageText="";
for(Blockblock:page.getBlocksList()){
StringblockText="";
for(Paragraphpara:block.getParagraphsList()){
StringparaText="";
for(Wordword:para.getWordsList()){
StringwordText="";
for(Symbolsymbol:word.getSymbolsList()){
wordText=wordText+symbol.getText();
System.out.format(
"Symbol text: %s (confidence: %f)%n",
symbol.getText(),symbol.getConfidence());
}
System.out.format(
"Word text: %s (confidence: %f)%n%n",wordText,word.getConfidence());
paraText=String.format("%s %s",paraText,wordText);
}
// Output Example using Paragraph:
System.out.println("%nParagraph: %n"+paraText);
System.out.format("Paragraph Confidence: %f%n",para.getConfidence());
blockText=blockText+paraText;
}
pageText=pageText+blockText;
}
}
  System.out.println("%nComplete annotation:");
System.out.println(annotation.getText());
}
}
}

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
constvision=require(&#39;@google-cloud/vision');
// Creates a client
constclient=newvision.ImageAnnotatorClient();
/**
 * TODO(developer): Uncomment the following line before running the sample.
 */
// const fileName = 'Local image file, e.g. /path/to/image.png';
// Read a local image as a text document
const[result]=awaitclient.documentTextDetection(fileName);
constfullTextAnnotation=result.fullTextAnnotation;
console.log(`Full text: ${fullTextAnnotation.text}`);
fullTextAnnotation.page>s.forEach(page={
page.blocks>.forEach(block={
console.log(`Block confidence: ${block.confidence}`);
block.paragraphs.for>Each(paragraph={
console.log(`Paragraph confidence: ${paragraph.confidence}`);
paragraph.word>s.forEach(word={
constwordText=word>.symbols.map(s=s.text).join('');
console.log(`Word text: ${wordText}`);
console.log(`Word confidence: ${word.confidence}`);
word.>symbols.forEach(symbol={
console.log(`Symbol text: ${symbol.text}`);
console.log(`Symbol confidence: ${symbol.confidence}`);
});
  });
});
});
});

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.

defdetect_document(path):
"""Detects document features in an image."""
 fromgoogle.cloudimport vision
 client = vision.ImageAnnotatorClient()
 with open(path, "rb") as image_file:
 content = image_file.read()
 image = vision.Image(content=content)
 response = client.document_text_detection(image=image)
 for page in response.full_text_annotation.pages:
 for block in page.blocks:
 print(f"\nBlock confidence: {block.confidence}\n")
 for paragraph in block.paragraphs:
 print("Paragraph confidence: {}".format(paragraph.confidence))
 for word in paragraph.words:
 word_text = "".join([symbol.text for symbol in word.symbols])
 print(
 "Word text: {} (confidence: {})".format(
 word_text, word.confidence
 )
 )
 for symbol in word.symbols:
 print(
 "\tSymbol: {} (confidence: {})".format(
 symbol.text, symbol.confidence
 )
 )
 if response.error.message:
 raise Exception(
 "{}\nFor more info on error messages, check: "
 "https://cloud.google.com/apis/design/errors".format(response.error.message)
 )

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.

Detect document text in a remote image

You can use the Vision API to perform feature detection on a remote image file that is located in Cloud Storage or on the Web. To send a remote file request, specify the file's Web URL or Cloud Storage URI in the request body.

REST

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

  • 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/handwriting_image.png
  • PROJECT_ID: Your Google Cloud project ID.

HTTP method and URL:

POST https://vision.googleapis.com/v1/images:annotate

Request JSON body:

{
 "requests": [
 {
 "image": {
 "source": {
 "imageUri": "CLOUD_STORAGE_IMAGE_URI"
 }
 },
 "features": [
 {
 "type": "DOCUMENT_TEXT_DETECTION"
 }
 ]
 }
 ]
}

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/images:annotate"

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/images:annotate" | Select-Object -Expand Content

If the request is successful, the server returns a 200 OK HTTP status code and the response in JSON format.

Response

{
 "responses": [
 {
 "textAnnotations": [
 {
 "locale": "en",
 "description": "O Google Cloud Platform\n",
 "boundingPoly": {
 "vertices": [
 {
 "x": 14,
 "y": 11
 },
 {
 "x": 279,
 "y": 11
 },
 {
 "x": 279,
 "y": 37
 },
 {
 "x": 14,
 "y": 37
 }
 ]
 }
 },
 ],
 "fullTextAnnotation": {
 "pages": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ]
 },
 "width": 281,
 "height": 44,
 "blocks": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ]
 },
 "boundingBox": {
 "vertices": [
 {
 "x": 14, "y": 11
 },
 {
 "x": 279, "y": 11
 },
 {
 "x": 279, "y": 37
 },
 {
 "x": 14, "y": 37
 }
 ]
 },
 "paragraphs": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ]
 },
 "boundingBox": {
 "vertices": [
 {
 "x": 14, "y": 11
 },
 {
 "x": 279, "y": 11
 },
 {
 "x": 279, "y": 37
 },
 {
 "x": 14, "y": 37
 }
 ]
 },
 "words": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ]
 },
 "boundingBox": {
 "vertices": [
 {
 "x": 14, "y": 11
 },
 {
 "x": 23, "y": 11
 },
 {
 "x": 23, "y": 37
 },
 {
 "x": 14, "y": 37
 }
 ]
 },
 "symbols": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ],
 "detectedBreak": {
 "type": "SPACE"
 }
 },
 "boundingBox": {
 "vertices": [
 {
 "x": 14, "y": 11
 },
 {
 "x": 23, "y": 11
 },
 {
 "x": 23, "y": 37
 },
 {
 "x": 14, "y": 37
 }
 ]
 },
 "text": "O"
 }
 ]
 },
 ]
 }
 ],
 "blockType": "TEXT"
 }
 ]
 }
 ],
 "text": "Google Cloud Platform\n"
 }
 }
 ]
}

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.


// detectDocumentText gets the full document text from the Vision API for an image at the given file path.
funcdetectDocumentTextURI(wio.Writer,filestring)error{
ctx:=context.Background()
client,err:=vision.NewImageAnnotatorClient(ctx)
iferr!=nil{
returnerr
}
image:=vision.NewImageFromURI(file)
annotation,err:=client.DetectDocumentText(ctx,image,nil)
iferr!=nil{
returnerr
}
ifannotation==nil{
fmt.Fprintln(w,"No text found.")
}else{
fmt.Fprintln(w,"Document Text:")
fmt.Fprintf(w,"%q\n",annotation.Text)
fmt.Fprintln(w,"Pages:")
for_,page:=rangeannotation.Pages{
fmt.Fprintf(w,"\tConfidence: %f, Width: %d, Height: %d\n",page.Confidence,page.Width,page.Height)
fmt.Fprintln(w,"\tBlocks:")
for_,block:=rangepage.Blocks{
fmt.Fprintf(w,"\t\tConfidence: %f, Block type: %v\n",block.Confidence,block.BlockType)
fmt.Fprintln(w,"\t\tParagraphs:")
for_,paragraph:=rangeblock.Paragraphs{
fmt.Fprintf(w,"\t\t\tConfidence: %f",paragraph.Confidence)
fmt.Fprintln(w,"\t\t\tWords:")
for_,word:=rangeparagraph.Words{
symbols:=make([]string,len(word.Symbols))
fori,s:=rangeword.Symbols{
symbols[i]=s.Text
}
wordText:=strings.Join(symbols,"")
fmt.Fprintf(w,"\t\t\t\tConfidence: %f, Symbols: %s\n",word.Confidence,wordText)
}
}
}
}
}
returnnil
}

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.

publicstaticvoiddetectDocumentTextGcs(StringgcsPath)throwsIOException{
List<AnnotateImageRequest>requests=newArrayList<>();
ImageSourceimgSource=ImageSource.newBuilder().setGcsImageUri(gcsPath).build();
Imageimg=Image.newBuilder().setSource(imgSource).build();
Featurefeat=Feature.newBuilder().setType(Type.DOCUMENT_TEXT_DETECTION).build();
AnnotateImageRequestrequest=
AnnotateImageRequest.newBuilder().addFeatures(feat).setImage(img).build();
requests.add(request);
// 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(ImageAnnotatorClientclient=ImageAnnotatorClient.create()){
BatchAnnotateImagesResponseresponse=client.batchAnnotateImages(requests)<;
ListAnnotateIma>geResponseresponses=response.getResponsesList();
client.close();
for(AnnotateImageResponseres:responses){
if(res.hasError()){
System.out.format("Error: %s%n",res.getError().getMessage());
return;
}
// For full list of available annotations, see http://g.co/cloud/vision/docs
TextAnnotationannotation=res.getFullTextAnnotation();
for(Pagepage:annotation.getPagesList()){
StringpageText="";
for(Blockblock:page.getBlocksList()){
StringblockText="";
for(Paragraphpara:block.getParagraphsList()){
StringparaText="";
for(Wordword:para.getWordsList()){
StringwordText="";
for(Symbolsymbol:word.getSymbolsList()){
wordText=wordText+symbol.getText();
System.out.format(
"Symbol text: %s (confidence: %f)%n",
symbol.getText(),symbol.getConfidence());
}
System.out.format(
"Word text: %s (confidence: %f)%n%n",wordText,word.getConfidence());
paraText=String.format("%s %s",paraText,wordText);
}
// Output Example using Paragraph:
System.out.println("%nParagraph: %n"+paraText);
System.out.format("Paragraph Confidence: %f%n",para.getConfidence());
blockText=blockText+paraText;
}
pageText=pageText+blockText;
}
}
  System.out.println("%nComplete annotation:");
System.out.println(annotation.getText());
}
}
}

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
constvision=require(&#39;@google-cloud/vision');
// Creates a client
constclient=newvision.ImageAnnotatorClient();
/**
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const bucketName = 'Bucket where the file resides, e.g. my-bucket';
// const fileName = 'Path to file within bucket, e.g. path/to/image.png';
// Read a remote image as a text document
const[result]=awaitclient.documentTextDetection(
`gs://${bucketName}/${fileName}`
);
constfullTextAnnotation=result.fullTextAnnotation;
console.log(fullTextAnnotation.text);

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.

defdetect_document_uri(uri):
"""Detects document features in the file located in Google Cloud
 Storage."""
 fromgoogle.cloudimport vision
 client = vision.ImageAnnotatorClient()
 image = vision.Image()
 image.source.image_uri = uri
 response = client.document_text_detection(image=image)
 for page in response.full_text_annotation.pages:
 for block in page.blocks:
 print(f"\nBlock confidence: {block.confidence}\n")
 for paragraph in block.paragraphs:
 print("Paragraph confidence: {}".format(paragraph.confidence))
 for word in paragraph.words:
 word_text = "".join([symbol.text for symbol in word.symbols])
 print(
 "Word text: {} (confidence: {})".format(
 word_text, word.confidence
 )
 )
 for symbol in word.symbols:
 print(
 "\tSymbol: {} (confidence: {})".format(
 symbol.text, symbol.confidence
 )
 )
 if response.error.message:
 raise Exception(
 "{}\nFor more info on error messages, check: "
  "https://cloud.google.com/apis/design/errors".format(response.error.message)
 )

gcloud

To perform handwriting detection, use the gcloud ml vision detect-document command as shown in the following example:

gcloud ml vision detect-document gs://cloud-samples-data/vision/handwriting_image.png

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.

Specify the language (optional)

Both types of OCR requests support one or more languageHints that specify the language of any text in the image. However, an empty value usually yields the best results, because omitting a value enables automatic language detection. For languages based on the Latin alphabet, setting languageHints is not needed. In rare cases, when the language of the text in the image is known, setting a hint helps get better results (although it can be a significant hindrance if the hint is wrong). Text detection returns an error if one or more of the specified languages is not one of the supported languages.

If you choose to provide a language hint, modify the body of your request (request.json file) to provide the string of one of the supported languages in the imageContext.languageHints field as shown in the following sample:

{
"requests":[
{
"image":{
"source&quot;:{
  "imageUri":"IMAGE_URL"
}
},
"features":[
{
  "type&quot;:"DOCUMENT_TEXT_DETECTION"
}
],
"imageContext":{
"languageHints":["en-t-i0-handwrit"]
}
}
]
}

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/handwriting_image.png
  • PROJECT_ID: Your Google Cloud project ID.

HTTP method and URL:

POST https://REGION_ID-vision.googleapis.com/v1/projects/PROJECT_ID/locations/REGION_ID/images:annotate

Request JSON body:

{
 "requests": [
 {
 "image": {
 "source": {
 "imageUri": "CLOUD_STORAGE_IMAGE_URI"
 }
 },
 "features": [
 {
 "type": "DOCUMENT_TEXT_DETECTION"
 }
 ]
 }
 ]
}

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/images:annotate"

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/images:annotate" | Select-Object -Expand Content

If the request is successful, the server returns a 200 OK HTTP status code and the response in JSON format.

Response

{
 "responses": [
 {
 "textAnnotations": [
 {
 "locale": "en",
 "description": "O Google Cloud Platform\n",
 "boundingPoly": {
 "vertices": [
 {
 "x": 14,
 "y": 11
 },
 {
 "x": 279,
 "y": 11
 },
 {
 "x": 279,
 "y": 37
 },
 {
 "x": 14,
 "y": 37
 }
 ]
 }
 },
 ],
 "fullTextAnnotation": {
 "pages": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ]
 },
 "width": 281,
 "height": 44,
 "blocks": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ]
 },
 "boundingBox": {
 "vertices": [
 {
 "x": 14, "y": 11
 },
 {
 "x": 279, "y": 11
 },
 {
 "x": 279, "y": 37
 },
 {
 "x": 14, "y": 37
 }
 ]
 },
 "paragraphs": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ]
 },
 "boundingBox": {
 "vertices": [
 {
 "x": 14, "y": 11
 },
 {
 "x": 279, "y": 11
 },
 {
 "x": 279, "y": 37
 },
 {
 "x": 14, "y": 37
 }
 ]
 },
 "words": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ]
 },
 "boundingBox": {
 "vertices": [
 {
 "x": 14, "y": 11
 },
 {
 "x": 23, "y": 11
 },
 {
 "x": 23, "y": 37
 },
 {
 "x": 14, "y": 37
 }
 ]
 },
 "symbols": [
 {
 "property": {
 "detectedLanguages": [
 {
 "languageCode": "en"
 }
 ],
 "detectedBreak": {
 "type": "SPACE"
 }
 },
 "boundingBox": {
 "vertices": [
 {
 "x": 14, "y": 11
 },
 {
 "x": 23, "y": 11
 },
 {
 "x": 23, "y": 37
 },
 {
 "x": 14, "y": 37
 }
 ]
 },
 "text": "O"
 }
 ]
 },
 ]
 }
 ],
 "blockType": "TEXT"
 }
 ]
 }
 ],
 "text": "Google Cloud Platform\n"
 }
 }
 ]
}

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.
funcsetEndpoint(endpointstring)error{
// endpoint := "eu-vision.googleapis.com:443"
ctx:=context.Background()
client,err:=vision.NewImageAnnotatorClient(ctx,option.WithEndpoint(endpoint))
iferr!=nil{
returnfmt.Errorf("NewImageAnnotatorClient: %w",err)
}
deferclient.Close()
returnnil
}

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.

ImageAnnotatorSettingssettings=
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.
ImageAnnotatorClientclient=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
constvision=require(&#39;@google-cloud/vision');
asyncfunctionsetEndpoint(){
// Specifies the location of the api endpoint
constclientOptions={apiEndpoint:'eu-vision.googleapis.com'};
// Creates a client
constclient=newvision.ImageAnnotatorClient(clientOptions);
// Performs text detection on the image file
const[result]=awaitclient.textDetection('./resources/wakeupcat.jpg&#39;);
constlabels=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.

fromgoogle.cloudimport vision
client_options = {"api_endpoint": "eu-vision.googleapis.com";}
client = vision. ImageAnnotatorClient(client_options=client_options)

Try it

Try text detection and document text detection in the following tool. You can use the image specified already (gs://cloud-samples-data/vision/handwriting_image.png) by clicking Execute, or you can specify your own image in its place.

Lined paper with Google Cloud Platform written in cursive.

Request body:

{
 "requests": [
 {
 "features": [
 {
 "type": "DOCUMENT_TEXT_DETECTION"
 }
 ],
 "image": {
 "source": {
 "imageUri": "gs://cloud-samples-data/vision/handwriting_image.png"
 }
 }
 }
 ]
}

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025年10月31日 UTC.