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Extract, Analyze, and Translate Text from Images with the Cloud ML APIs

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Extract, Analyze, and Translate Text from Images with the Cloud ML APIs

50 minutes 7 Credits

GSP075

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Overview

In this lab, we'll explore the power of machine learning by using multiple machine learning APIs together. We'll start with the Cloud Vision API's text detection method to make use of Optical Character Recognition (OCR) to extract text from images. Then we'll learn how to translate that text with the Translation API and analyze it with the Natural Language API.

What you'll learn

  • Creating a Vision API request and calling the API with curl

  • Using the text detection (OCR) method of the Vision API

  • Using the Translation API to translate text from your image

  • Using the Natural Language API to analyze the text

Setup and Requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

  • Access to a standard internet browser (Chrome browser recommended).
Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.
  • Time to complete the lab---remember, once you start, you cannot pause a lab.
Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

How to start your lab and sign in to the Google Cloud Console

  1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

    • The Open Google Console button
    • Time remaining
    • The temporary credentials that you must use for this lab
    • Other information, if needed, to step through this lab
  2. Click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.

    Tip: Arrange the tabs in separate windows, side-by-side.

    Note: If you see the Choose an account dialog, click Use Another Account.
  3. If necessary, copy the Username from the Lab Details panel and paste it into the Sign in dialog. Click Next.

  4. Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.

    Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.
  5. Click through the subsequent pages:

    • Accept the terms and conditions.
    • Do not add recovery options or two-factor authentication (because this is a temporary account).
    • Do not sign up for free trials.

After a few moments, the Cloud Console opens in this tab.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left. Navigation menu icon

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

  1. Click Activate Cloud Shell Activate Cloud Shell icon at the top of the Google Cloud console.

When you are connected, you are already authenticated, and the project is set to your PROJECT_ID. The output contains a line that declares the PROJECT_ID for this session:

Your Cloud Platform project in this session is set to YOUR_PROJECT_ID

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

  1. (Optional) You can list the active account name with this command:

gcloud auth list
  1. Click Authorize.

  2. Your output should now look like this:

Output:

ACTIVE: * ACCOUNT: student-01-xxxxxxxxxxxx@qwiklabs.net To set the active account, run: $ gcloud config set account `ACCOUNT`
  1. (Optional) You can list the project ID with this command:

gcloud config list project

Output:

[core] project = <project_ID>

Example output:

[core] project = qwiklabs-gcp-44776a13dea667a6 Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

Create an API Key

Since you'll be using curl to send a request to the Vision API, you'll need to generate an API key to pass in your request URL. To create an API key, navigate to:

Navigation Menu > APIs & services > Credentials:

api_nav.png

Click + Create Credentials.

In the drop down menu, select API key:

api_key.png

Next, copy the key you just generated. Click Close.

Now save the API key to an environment variable to avoid having to insert the value of your API key in each request.

Run the following in Cloud Shell, replacing <your_api_key> with the key you just copied.

export API_KEY=<YOUR_API_KEY>

Click Check my progress to verify your performed task.

Create an API Key

Upload an image to a cloud storage bucket

Creating a Cloud Storage bucket

There are two ways to send an image to the Vision API for image detection: by sending the API a base64 encoded image string, or passing it the URL of a file stored in Cloud Storage. For this lab you'll create a Cloud Storage bucket to store your images.

Navigate to the Navigation menu > Cloud Storage browser in the Console:

storage_bucket.png

Then click Create bucket.

Give your bucket a globally unique name and click on Choose how to control access to objects.

Uncheck the box for Enforce public access prevention on this bucket.

Choose Fine-grained under Access Control and click Create

fine-grained.png

Upload an image to your bucket

Right click on the following image of a French sign, then click Save image as and save it to your computer as sign.jpg.

sign_image.png

Navigate to the bucket you just created in the cloud storage browser and click Upload files. Then select sign.jpg.

Next you'll allow the file to be viewed publicly while keeping the access to the bucket private.

Click on the 3 dots for the image file:

bucket_object_3dots.png

Select Edit Access.

Click Add Entry and set the following:

  • Select Public for the Entity.
  • Ensure allUsers is the value for the Name.
  • Select Reader for the Access.

bucket_object_public_perm.png

Click Save.

You'll now see that the file has public access.

Now that you have the file in your bucket, you're ready to create a Vision API request, passing it the URL of this picture.

Click Check my progress to verify your performed task.

Upload image to a bucket

Create your Vision API request

In your Cloud Shell environment, create an ocr-request.json files, then add the code below to the file, replacing my-bucket-name with the name of the bucket you created. You can create the file using one of your preferred command line editors (nano, vim, emacs) or click the pencil icon to open the code editor in Cloud Shell:

pencil.png

Add the following to your ocr-request.json file:

{ "requests": [ { "image": { "source": { "gcsImageUri": "gs://my-bucket-name/sign.jpg" } }, "features": [ { "type": "TEXT_DETECTION", "maxResults": 10 } ] } ] }

You're going to use the TEXT_DETECTION feature of the Vision API. This will run optical character recognition (OCR) on the image to extract text.

Call the Vision API's text detection method

In Cloud Shell, call the Vision API with curl:

curl -s -X POST -H "Content-Type: application/json" --data-binary @ocr-request.json https://vision.googleapis.com/v1/images:annotate?key=${API_KEY}

The first part of your response should look like the following:

{ "responses": [ { "textAnnotations": [ { "locale": "fr", "description": "LE BIEN PUBLIC\nles dépêches\nPour Obama,\nla moutarde\nest\nde Dijon\n", "boundingPoly": { "vertices": [ { "x": 138, "y": 40 }, { "x": 622, "y": 40 }, { "x": 622, "y": 795 }, { "x": 138, "y": 795 } ] } }, { "description": "LE", "boundingPoly": { "vertices": [ { "x": 138, "y": 99 }, { "x": 274, "y": 82 }, { "x": 283, "y": 157 }, { "x": 147, "y": 173 } ] } }, { "description": "BIEN", "boundingPoly": { "vertices": [ { "x": 291, "y": 79 }, { "x": 413, "y": 64 }, { "x": 422, "y": 139 }, { "x": 300, "y": 154 } ] } ... ] }] }

The OCR method is able to extract lots of text from our image, cool! Let's break down the response. The first piece of data you get back from textAnnotations is the entire block of text the API found in the image. This includes the language code (in this case fr for French), a string of the text, and a bounding box indicating where the text was found in our image. Then you get an object for each word found in the text with a bounding box for that specific word.

Note: The Vision API also has a DOCUMENT_TEXT_DETECTION feature optimized for images with more text. This response includes additional information and breaks text down into page, blocks, paragraphs, and words.

Unless you speak French you probably don't know what this says. The next step is translation.

Run the following curl command to save the response to an ocr-response.json file so it can be referenced later:

curl -s -X POST -H "Content-Type: application/json" --data-binary @ocr-request.json https://vision.googleapis.com/v1/images:annotate?key=${API_KEY} -o ocr-response.json

Sending text from the image to the Translation API

The Translation API can translate text into 100+ languages. It can also detect the language of the input text. To translate the French text into English, all you need to do is pass the text and the language code for the target language (en-US) to the Translation API.

First, create a translation-request.json file and add the following to it:

{ "q": "your_text_here", "target": "en" }

q is where you'll pass the string to translate.

Save the file.

Run this Bash command in Cloud Shell to extract the image text from the previous step and copy it into a new translation-request.json (all in one command):

STR=$(jq .responses[0].textAnnotations[0].description ocr-response.json) && STR="${STR//\"}" && sed -i "s|your_text_here|$STR|g" translation-request.json

Now you're ready to call the Translation API. This command will also copy the response into a translation-response.json file:

curl -s -X POST -H "Content-Type: application/json" --data-binary @translation-request.json https://translation.googleapis.com/language/translate/v2?key=${API_KEY} -o translation-response.json

Run this command to inspect the file with the Translation API response:

cat translation-response.json

Awesome, you can understand more of what the sign said!

{ "data": { "translations": [ { "translatedText": "TO THE PUBLIC GOOD the dispatches For Obama, the mustard is from Dijon", "detectedSourceLanguage": "fr" } ] } }

In the response, translatedText contains the resulting translation, and detectedSourceLanguage is fr, the ISO language code for French. The Translation API supports 100+ languages, all of which are listed here.

In addition to translating the text from our image, you might want to do more analysis on it. That's where the Natural Language API comes in handy. Onward to the next step!

Analyzing the image's text with the Natural Language API

The Natural Language API helps us understand text by extracting entities, analyzing sentiment and syntax, and classifying text into categories. Use the analyzeEntities method to see what entities the Natural Language API can find in the text from your image.

To set up the API request, create a nl-request.json file with the following:

{ "document":{ "type":"PLAIN_TEXT", "content":"your_text_here" }, "encodingType":"UTF8" }

In the request, you're telling the Natural Language API about the text you're sending:

type: Supported type values are PLAIN_TEXT or HTML.

content: pass the text to send to the Natural Language API for analysis. The Natural Language API also supports sending files stored in Cloud Storage for text processing. To send a file from Cloud Storage, you would replace content with gcsContentUri and use the value of the text file's uri in Cloud Storage.

encodingType: tells the API which type of text encoding to use when processing the text. The API will use this to calculate where specific entities appear in the text.

Run this Bash command in Cloud Shell to copy the translated text into the content block of the Natural Language API request:

STR=$(jq .data.translations[0].translatedText translation-response.json) && STR="${STR//\"}" && sed -i "s|your_text_here|$STR|g" nl-request.json

The nl-request.json file now contains the translated English text from the original image. Time to analyze it!

Call the analyzeEntities endpoint of the Natural Language API with this curl request:

curl "https://language.googleapis.com/v1/documents:analyzeEntities?key=${API_KEY}" \ -s -X POST -H "Content-Type: application/json" --data-binary @nl-request.json

If you scroll through the response you can see the entities the Natural Language API found:

{ "entities": [ { "name": "dispatches", "type": "OTHER", "metadata": {}, "salience": 0.3560996, "mentions": [ { "text": { "content": "dispatches", "beginOffset": 23 }, "type": "COMMON" } ] }, { "name": "mustard", "type": "OTHER", "metadata": {}, "salience": 0.2878307, "mentions": [ { "text": { "content": "mustard", "beginOffset": 38 }, "type": "COMMON" } ] }, { "name": "Obama", "type": "PERSON", "metadata": { "mid": "/m/02mjmr", "wikipedia_url": "https://en.wikipedia.org/wiki/Barack_Obama" }, "salience": 0.16260329, "mentions": [ { "text": { "content": "Obama", "beginOffset": 31 }, "type": "PROPER" } ] }, { "name": "Dijon", "type": "LOCATION", "metadata": { "mid": "/m/0pbhz", "wikipedia_url": "https://en.wikipedia.org/wiki/Dijon" }, "salience": 0.08129317, "mentions": [ { "text": { "content": "Dijon", "beginOffset": 54 }, "type": "PROPER" } ] } ], "language": "en" }

For entities that have a wikipedia page, the API provides metadata including the URL of that page along with the entity's mid. The mid is an ID that maps to this entity in Google's Knowledge Graph. To get more information on it, you could call the Knowledge Graph API, passing it this ID. For all entities, the Natural Language API tells us the places it appeared in the text (mentions), the type of entity, and salience (a [0,1] range indicating how important the entity is to the text as a whole). In addition to English, the Natural Language API also supports the languages listed here.

Looking at this image it's relatively easy for us to pick out the important entities, but if we had a library of thousands of images this would be much more difficult. OCR, translation, and natural language processing can help to extract meaning from large datasets of images.

Click Check my progress to verify your performed task.

Analyzing the image's text with the Natural Language API

Congratulations!

You've learned how to combine 3 different machine learning APIs: the Vision API's OCR method extracted text from an image, then the Translation API translated that text to English and the Natural Language API to found entities in that text.

What was covered

  • Use cases for combining multiple machine learning APIs
  • Creating a Vision API OCR request and calling the API with curl
  • Translating text with the Translation API
  • Extract entities from text with the Natural Language API

ml_quest_icon.png ML-Image-Processing-badge.png

Finish your quest

This self-paced lab is part of the Machine Learning APIs and Intro to ML: Image Processing Quests. A Quest is a series of related labs that form a learning path. Completing a Quest earns you a badge to recognize your achievement. You can make your badge (or badges) public and link to them in your online resume or social media account. Enroll in these Quests and get immediate completion credit if you've taken this lab. See other available Quests.

Take your next lab

Try out another lab on Machine Learning APIs, like Classify Text into Categories using the Natural Language API or Awwvision: Cloud Vision API from a Kubernetes Cluster.

Next steps

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Manual Last Updated September 22, 2022
Lab Last Tested September 22, 2022

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