GSP1139

Overview

Document AI is a document understanding solution that takes unstructured data (e.g. documents, emails, invoices, forms, etc.) and makes the data easier to understand, analyze, and consume. The API provides structure through content classification, entity extraction, advanced searching, and more.

In this lab, you will learn how to use the Document AI Form Parser to parse a handwritten form with Python. You will use a simple medical intake form as an example, but this procedure will work with any generalized form supported by DocAI.

Objectives

In this lab, you will learn how to perform the following tasks:

• Extract data from a scanned form using the Document AI Form Parser
• Extract key/value pairs from a form using the Document AI Form Parser
• Extract and export CSV data from a form using the Document AI Form Parser

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 Cloud 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 Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   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 below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details panel.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details panel.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. 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 Google Cloud console opens in this tab.

Note: To view a menu with a list of Google Cloud products and services, click the Navigation menu at the top-left.

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 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 {{{project_0.project_id | "PROJECT_ID"}}}

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

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

gcloud auth list

3. Click Authorize.

Output:

ACTIVE: * ACCOUNT: {{{user_0.username | "ACCOUNT"}}} To set the active account, run: $ gcloud config set account `ACCOUNT`

4. (Optional) You can list the project ID with this command:

gcloud config list project

Output:

[core] project = {{{project_0.project_id | "PROJECT_ID"}}} Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

Task 1. Enable the Document AI API

Before you can begin using Document AI, you must enable the API.

1. Open Cloud Shell by clicking the Activate Cloud Shell button at the top of the console.

2. In Cloud Shell, run the following commands to enable the API for Document AI.

gcloud services enable documentai.googleapis.com

You should see something like this:

Operation "operations/..." finished successfully.

You will also need to install Pandas, an Open Source Data Analysis library for Python.

3. Run the following command to install Pandas.

pip3 install --upgrade pandas

4. Run the following command to install the Python client libraries for Document AI.

pip3 install --upgrade google-cloud-documentai

You should see something like this:

... Installing collected packages: google-cloud-documentai Successfully installed google-cloud-documentai-2.15.0

Now, you're ready to use the Document AI API!

Click Check my progress to verify the objective.

Enable the Document AI API.

Task 2. Create a Form Parser processor

You must first create a Form Parser processor instance to use in the Document AI Platform for this tutorial.

1. In the Cloud console, open the Navigation menu (), click View All Products > Artificial Intelligence > Document AI.

2. Click Explore Processors, and inside Form Parser, click Create Processor.

3. Give it the name lab-form-parser and select the closest region on the list.

4. Click Create to create your processor

5. Copy your Processor ID. You must use this in your code later.

Click Check my progress to verify the objective.

Create a processor

Test the processor in the Cloud Console

You can test out your processor in the console by uploading a document.

1. Right click the image below, and select Save Image As to download the sample form.

2. On the Processor Details page, click Upload Test Document. Select the form you just downloaded.

Your Form Parser processor will process the document and return the parsed form data. It should look something like this:

Task 3. Download the sample form

In this section, you will download a sample document which contains a simple medical intake form.

1. Run the following command to download the sample form to your Cloud Shell.

gcloud storage cp gs://cloud-samples-data/documentai/codelabs/form-parser/intake-form.pdf .

2. Confirm the file is downloaded to your Cloud Shell using the below command:

ls intake-form.pdf

Task 4. Extract form key/value pairs

In this section, you will use the online processing API to call the Form Parser processor you created previously. Then, you will extract the key value pairs found in the document.

Online processing is for sending a single document and waiting for the response. You can also use batch processing if you want to send multiple files or if the file size exceeds the online processing maximum pages.

The code for making a process request is identical for every processor type aside from the Processor ID. The Document response object contains a list of pages from the input document. Each page object contains a list of form fields and their locations in the text.

The following code iterates through each page and extracts each key, value and confidence score. This is structured data that can more easily stored in databases or used in other applications.

1. In Cloud Shell, create a file called form_parser.py and paste the following code into it:

import pandas as pd from google.cloud import documentai_v1 as documentai def online_process( project_id: str, location: str, processor_id: str, file_path: str, mime_type: str, ) -> documentai.Document: """ Processes a document using the Document AI Online Processing API. """ opts = {"api_endpoint": f"{location}-documentai.googleapis.com"} # Instantiates a client documentai_client = documentai.DocumentProcessorServiceClient(client_options=opts) # The full resource name of the processor, e.g.: # projects/project-id/locations/location/processor/processor-id # You must create new processors in the Cloud Console first resource_name = documentai_client.processor_path(project_id, location, processor_id) # Read the file into memory with open(file_path, "rb") as image: image_content = image.read() # Load Binary Data into Document AI RawDocument Object raw_document = documentai.RawDocument( content=image_content, mime_type=mime_type ) # Configure the process request request = documentai.ProcessRequest( name=resource_name, raw_document=raw_document ) # Use the Document AI client to process the sample form result = documentai_client.process_document(request=request) return result.document def trim_text(text: str): """ Remove extra space characters from text (blank, newline, tab, etc.) """ return text.strip().replace("\n", " ") PROJECT_ID = "YOUR_PROJECT_ID" LOCATION = "YOUR_PROJECT_LOCATION" # Format is 'us' or 'eu' PROCESSOR_ID = "FORM_PARSER_ID" # Create processor in Cloud Console # The local file in your current working directory FILE_PATH = "form.pdf" # Refer to https://cloud.google.com/document-ai/docs/processors-list # for supported file types MIME_TYPE = "application/pdf" document = online_process( project_id=PROJECT_ID, location=LOCATION, processor_id=PROCESSOR_ID, file_path=FILE_PATH, mime_type=MIME_TYPE, ) names = [] name_confidence = [] values = [] value_confidence = [] for page in document.pages: for field in page.form_fields: # Get the extracted field names names.append(trim_text(field.field_name.text_anchor.content)) # Confidence - How "sure" the Model is that the text is correct name_confidence.append(field.field_name.confidence) values.append(trim_text(field.field_value.text_anchor.content)) value_confidence.append(field.field_value.confidence) # Create a Pandas Dataframe to print the values in tabular format. df = pd.DataFrame( { "Field Name": names, "Field Name Confidence": name_confidence, "Field Value": values, "Field Value Confidence": value_confidence, } ) print(df)

2. Replace YOUR_PROJECT_ID, YOUR_PROJECT_LOCATION, YOUR_PROCESSOR_ID, and the FILE_PATH with appropriate values for your environment.

Note that the FILE_PATH is the name of the file you downloaded to Cloud Shell in the previous step. If you didn't rename the file, it should be intake-form.pdf, which you will need to update in the code.

3. Run the following command to execute the script:

python3 form_parser.py

You should see the following output:

Field Name Field Name Confidence Field Value Field Value Confidence 0 Phone #: 0.999982 (906) 917-3486 0.999982 1 Emergency Contact: 0.999972 Eva Walker 0.999972 2 Marital Status: 0.999951 Single 0.999951 3 Gender: 0.999933 F 0.999933 4 Occupation: 0.999914 Software Engineer 0.999914 5 Referred By: 0.999862 None 0.999862 6 Date: 0.999858 9/14/19 0.999858 7 DOB: 0.999716 09/04/1986 0.999716 8 Address: 0.999147 24 Barney Lane 0.999147 9 City: 0.997718 Towaco 0.997718 10 Name: 0.997345 Sally Walker 0.997345 11 State: 0.996944 NJ 0.996944 ...

Task 5. Parse tables

The Form Parser is also able to extract data from tables within documents. In this section, you will download a new sample document and extract data from the table. Since you are loading the data into Pandas, this data can be output to a CSV file and many other formats with a single method call.

Download the Sample Form with Tables

We have a sample document which contains a sample form and a table.

1. Run the following command to download the sample form to your Cloud Shell.

gcloud storage cp gs://cloud-samples-data/documentai/codelabs/form-parser/form_with_tables.pdf .

2. Confirm the file is downloaded to your Cloud Shell using the below command:

ls form_with_tables.pdf

Extract Table Data

The processing request for table data is exactly the same as for extracting key-value pairs. The difference is which fields you extract the data from in the response. Table data is stored in the pages[].tables[] field.

This example extracts information about from the table header rows and body rows for each table and page, then prints out the table and saves the table as a CSV file.

1. Create a file called table_parsing.py and paste the following code into it:

# type: ignore[1] """ Uses Document AI online processing to call a form parser processor Extracts the tables and data in the document. """ from os.path import splitext from typing import List, Sequence import pandas as pd from google.cloud import documentai def online_process( project_id: str, location: str, processor_id: str, file_path: str, mime_type: str, ) -> documentai.Document: """ Processes a document using the Document AI Online Processing API. """ opts = {"api_endpoint": f"{location}-documentai.googleapis.com"} # Instantiates a client documentai_client = documentai.DocumentProcessorServiceClient(client_options=opts) # The full resource name of the processor, e.g.: # projects/project-id/locations/location/processor/processor-id # You must create new processors in the Cloud Console first resource_name = documentai_client.processor_path(project_id, location, processor_id) # Read the file into memory with open(file_path, "rb") as image: image_content = image.read() # Load Binary Data into Document AI RawDocument Object raw_document = documentai.RawDocument( content=image_content, mime_type=mime_type ) # Configure the process request request = documentai.ProcessRequest( name=resource_name, raw_document=raw_document ) # Use the Document AI client to process the sample form result = documentai_client.process_document(request=request) return result.document def get_table_data( rows: Sequence[documentai.Document.Page.Table.TableRow], text: str ) -> List[List[str]]: """ Get Text data from table rows """ all_values: List[List[str]] = [] for row in rows: current_row_values: List[str] = [] for cell in row.cells: current_row_values.append( text_anchor_to_text(cell.layout.text_anchor, text) ) all_values.append(current_row_values) return all_values def text_anchor_to_text(text_anchor: documentai.Document.TextAnchor, text: str) -> str: """ Document AI identifies table data by their offsets in the entirety of the document's text. This function converts offsets to a string. """ response = "" # If a text segment spans several lines, it will # be stored in different text segments. for segment in text_anchor.text_segments: start_index = int(segment.start_index) end_index = int(segment.end_index) response += text[start_index:end_index] return response.strip().replace("\n", " ") PROJECT_ID = "YOUR_PROJECT_ID" LOCATION = "YOUR_PROJECT_LOCATION" # Format is 'us' or 'eu' PROCESSOR_ID = "FORM_PARSER_ID" # Create processor before running sample # The local file in your current working directory FILE_PATH = "form_with_tables.pdf" # Refer to https://cloud.google.com/document-ai/docs/file-types # for supported file types MIME_TYPE = "application/pdf" document = online_process( project_id=PROJECT_ID, location=LOCATION, processor_id=PROCESSOR_ID, file_path=FILE_PATH, mime_type=MIME_TYPE, ) header_row_values: List[List[str]] = [] body_row_values: List[List[str]] = [] # Input Filename without extension output_file_prefix = splitext(FILE_PATH)[0] for page in document.pages: for index, table in enumerate(page.tables): header_row_values = get_table_data(table.header_rows, document.text) body_row_values = get_table_data(table.body_rows, document.text) # Create a Pandas Dataframe to print the values in tabular format. df = pd.DataFrame( data=body_row_values, columns=pd.MultiIndex.from_arrays(header_row_values), ) print(f"Page {page.page_number} - Table {index}") print(df) # Save each table as a CSV file output_filename = f"{output_file_prefix}_pg{page.page_number}_tb{index}.csv" df.to_csv(output_filename, index=False)

2. Replace YOUR_PROJECT_ID, YOUR_PROJECT_LOCATION, YOUR_PROCESSOR_ID, and the FILE_PATH with appropriate values for your environment.

Note that the FILE_PATH is the name of the file you downloaded to Cloud Shell in the previous step. If you didn't rename the file, it should be form_with_tables.pdf, which is the default value and doesn't need to be changed.

3. Run the following command to execute the script:

python3 table_parsing.py

You should see the following output:

Page 1 - Table 0 Item Description 0 Item 1 Description 1 1 Item 2 Description 2 2 Item 3 Description 3

You should also have a new CSV file in the directory you are running the code from.

4. Run the following command to list the files in your current working directory:

ls

You should see the following output:

form_with_tables_pg1_tb0.csv

Congratulations!

Congratulations, in this lab you've successfully used the Document AI API to extract data from a handwritten form. You also learned how to use the Document AI Python client library to extract key-value pairs from a form and how to extract tabular data from a form with tables.

Next steps / Learn more

Check out the following resources to learn more about Document AI and the Python Client Library:

• Document AI Documentation
• Document AI Python Client Library
• Document AI Samples

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Manual Last Updated November 13, 2024

Lab Last Tested November 13, 2024

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