# Extract information from unstructured documents with Amazon Bedrock and Amazon Textract
| | |
| --- |--- |
| **AWS experience** | Beginner |
| **Time to complete** | 20 minutes |
| **Cost to complete** | Less than \$10.15 if completed within two hours and the notebook is deleted at the end of the tutorial. |
| **Get help** | [Troubleshooting Amazon Bedrock models](https://docs.aws.amazon.com/bedrock/latest/userguide/fine-tuning-troubleshooting.html) [Debugging traning issues](https://docs.aws.amazon.com/textract/latest/dg/textract-debugging-failures-adapters.html) |
| **Last update** | November 14, 2024 |
## Overview
In this tutorial, you will learn how to utilize Amazon Bedrock and Amazon Textract to extract and process information from unstructured documents.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.
Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, layout elements, and data from scanned documents.
## What you will accomplish
In this tutorial, you will:
+ Enable access to a foundation model on your AWS account
+ Create a new Jupyter notebook to write test code and run tests
+ Generate code
+ Clean up your resources
## Prerequisites
Before starting this tutorial, you will need:
+ An AWS account: if you don't already have one follow the [Setup Your Environment](https://docs.aws.amazon.com/hands-on/latest/setup-environment/) tutorial.
## Implementation
### Step 1: Enable Anthropic FM
In this step, you will enable the use of Anthropic models on your AWS account.
**Note**
**Already requested and obtained access to Anthropic models on Amazon Bedrock?** Skip to [Step 2: Create a Jupyter Notebook](#create-a-jupyter-notebook).
1. Open Amazon Bedrock
**Sign in** to the AWS Management console, and **open** the Amazon Bedrock console at [https://console.aws.amazon.com/bedrock/home](https://console.aws.amazon.com/bedrock/home).
In the left navigation pane, under **Bedrock configurations**, choose **Model Access**.
![\[The navigation interface.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/navigation-interface.png)
1. Enable a model
On the **What is Model access?** page, choose **Enable specific models**.
![\[Amazon Bedrock model access page with options to enable all models or enable specific models, and links to IAM permissions and quotas.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/bedrock-model-access-page-options-enable.png)
1. Choose the Anthropic models
On the **Edit model access** page, select the **Anthropic models**, and choose **Next**.
![\[A user interface for editing model access, showing a list of AI models grouped by provider, with all models under "Anthropic" selected and the "Next" button highlighted.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/user-interface-editing-model-access-list.png)
1. Review and submit the change
On the **Review and submit** page, review your selections, and choose **Submit**.
![\[Interface for editing model access in AWS, listing eight Claude models with 'Request access' options, and a 'Submit' button highlighted at the bottom.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/interface-editing-model-access-listing.png)
### Step 2: Create a Jupyter Notebook
In this step, you will create a Jupyter notebook to write your Proof-of-Concept code and test it out with real documents.
1. Open Amazon SageMaker AI
**Open** the Amazon Sagemaker console at [https://console.aws.amazon.com/sagemaker/home](https://console.aws.amazon.com/sagemaker/home).
In the left navigation pane, under **Applications and IDEs**, choose **Notebooks**.
![\[The navigation interface.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/navigation-interface-1.png)
1. Create a notebook instance
On the **Notebooks and Git repos** page, choose **Create notebook instance**.
![\[Amazon SageMaker AI interface showing the "Notebook instances" tab with no resources listed and an orange "Create notebook instance" button highlighted.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/interface-notebook-instances-tab-resources.png)
1. Configure notebook instance settings
On the **Create notebook instance** page:
In the **Notebook instance settings** section:
+ For **Notebook instance name**, enter a **name** for your Jupyter instance.
+ For **Notebook instance type**, verify **ml.t3.medium** is selected.
+ Keep all other default settings.
![\[Amazon SageMaker AI interface for creating a notebook instance, showing settings for instance name, type, IAM role creation success, and the 'Create notebook instance' button highlighted.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/interface-creating-notebook-instance.png)
1. Configure permissions and encryption
In the **Permissions and encryption** section:
+ For **IAM role**, choose **Create a new role**.
+ On the **Create an IAM role** pop up window, for **S3 buckets you specify** – **optional**, choose **None**, and then choose **Create role**.
Then, choose **Create notebook instance**.
![\[AWS IAM role creation screen with S3 bucket access options, 'None' selected, and 'Create role' button highlighted.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/iam-role-creation-screen-bucket-access.png)
### Step 3: Generate code to process your documents
In this step, you will use Bedrock playground to generate code for your Jupyter notebook.
1. Open JupyterLab
On the **Notebook instance** page, choose **Open JupyterLab** for the instance you created in the previous step.
**Note**
The notebook will open in a separate browser tab.
![\[The navigation interface.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/navigation-interface-2.png)
1. Create a new notebook
On the **JupyterLab** tab, right-click the file area, and then select **New Notebook**.
![\[A JupyterLab interface showing a context menu with the "New Notebook" option highlighted in red.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/jupyterlab-interface-context-menu-new.png)
1. Select kernel
On the **Select Kernel** pop up window, choose **conda\$1python3**, and choose **Select**.
![\[Dialog box titled "Select Kernel" with a dropdown showing "conda_python3" selected and two buttons: "No Kernel" and "Select.".\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/dialog-box-titled-select-kernel-dropdown.png)
1. Open the chat playground
In a new tab, **open** the Amazon Bedrock console at [https://console.aws.amazon.com/bedrock/home](https://console.aws.amazon.com/bedrock/home).
In the left navigation pane, under **Playgrounds**, choose **Chat/text**.
![\[Amazon Bedrock interface showing navigation options on the left, including "Playgrounds" with "Chat/text" highlighted, and foundation model options on the right.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/bedrock-interface-navigation-options-left.png)
1. Select the model
On the **Mode** page, choose **Select model**.
![\[Amazon Bedrock interface showing a "Select model" button highlighted in orange, with options for input, output, and latency.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/bedrock-interface-select-model-button.png)
1. Specify the model details
In the **Select model** dialog box:
+ For **Categories**, choose **Anthropic**.
+ For **Models with access**, choose the **Claude 3.5 Sonnet** model.
+ Then, choose **Apply**.
**Note**
The Claude 3.5 Sonnet is the most intelligent model from Anthropic. You can see a more detailed model comparison [here](https://docs.anthropic.com/en/docs/about-claude/models).
![\[A model selection interface showing "Anthropic" as the chosen provider, "Claude 3.5 Sonnet" as the selected model, and an "Apply" button highlighted in orange.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/model-selection-interface-anthropic-chosen.png)
1. Generate code
In the **Chat playground**, you can now ask the LLM to write sample code. The following is an example prompt that you can use to extract information from an unstructured document.
```
I am writing a Jupyter notebook with a proof of concept python code snippets to perform a few tasks.
To start, write a snippet to iterate the current folder and read all the jpg/png files and for each file call textract DetectDocumentText API to extract all the text on the image.
Re-save the result with the same file name and txt extension.
Also make sure to:
- Not reprocess any files that already have the txt file existing in the directory
- Print a progress bar output using tdqm
- Keep everything readable and properly componentized in methods
- No need for __main__ implementations as it's a snippet to run on Jupyter notebook.
```
Once you enter your prompt, and choose **Run**, the prompt response will include code and also explanation of everything that the model generated. The code will typically be enclosed in quotation marks.
![\[Amazon Bedrock's Chat/text playground interface with a prompt about writing a Python script for text extraction using the DetectDocumentText API in a Jupyter notebook.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/bedrock-chat-text-playground-interface.png)
1. Check the output
The generated code with the example prompt should look similar to the following example. You can also use the **copy** function to **paste** the code directly into the Jupyter notebook.
```
import os
import boto3
from tqdm import tqdm
from PIL import Image
def get_image_files(directory):
"""Get all jpg and png files in the given directory."""
return [f for f in os.listdir(directory) if f.lower().endswith(('.jpg', '.png'))]
def should_process_file(file_path):
"""Check if the file should be processed (i.e., no corresponding txt file exists)."""
txt_path = os.path.splitext(file_path)[0] + '.txt'
return not os.path.exists(txt_path)
def extract_text_from_image(image_path):
"""Extract text from the image using Amazon Textract."""
client = boto3.client('textract')
with open(image_path, 'rb') as image:
response = client.detect_document_text(Document={'Bytes': image.read()})
extracted_text = []
for item in response['Blocks']:
if item['BlockType'] == 'LINE':
extracted_text.append(item['Text'])
return '\n'.join(extracted_text)
def save_text_to_file(text, file_path):
"""Save the extracted text to a file."""
txt_path = os.path.splitext(file_path)[0] + '.txt'
with open(txt_path, 'w', encoding='utf-8') as f:
f.write(text)
def process_images_in_directory(directory):
"""Process all images in the given directory."""
image_files = get_image_files(directory)
for image_file in tqdm(image_files, desc="Processing images"):
image_path = os.path.join(directory, image_file)
if should_process_file(image_path):
extracted_text = extract_text_from_image(image_path)
save_text_to_file(extracted_text, image_path)
# Usage in Jupyter notebook
directory = '.' # Current directory
process_images_in_directory(directory)
```
**Note**
The previous example code is built to process all files on the current directory and needs an image in order to fully process the code.
![\[A coding interface showing a Python script for processing image files and extracting text using Amazon Textract, with a highlighted copy icon in the top-right corner.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/coding-interface-processing-image-files.png)
1. Prepare your image file
You can use your own image or download and save this [image](images/health_insurance_card_redacted.png). Then, **find** the image you want to use on your local machine, and **drag** the file to the Jupyter Notebook file explorer in order to copy and paste it.
![\[A Jupyter Notebook showing Python code for processing image files and extracting text using AWS Amazon Textract, alongside a file browser displaying a health insurance card image file.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/jupyter-notebook-code-processing-image.png)
1. Configure permissions
Before you can run the code in your JupyterLab, the IAM role that was previously created for your Jupyter notebook, needs the appropriate permissions to run the AWS services that your code is going to use. If you chose to use the previous example, Amazon Textract is the AWS service that would need the appropriate permissions.
**Open** the AWS IAM console at [https://console.aws.amazon.com/iam/home](https://console.aws.amazon.com/iam/home).
In the left navigation pane, choose **Roles**.
![\[Identity and Access Management (IAM) menu with 'Roles' highlighted in red under Access management.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/identity-access-management-iam-menu-roles.png)
1. Search for the IAM role
In the **search box**, find the previously created ****AmazonSageMaker AI-ExecutionRole-**** role, and **open** the role.
![\[The AWS IAM Roles page showing a search for "AmazonSageMaker AI-ExecutionRole" with one matching result listed.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/iam-roles-page-search-executionrole-one.png)
1. Add permissions
On the **AmazonSageMaker AI-ExecutionRole-** page, choose the **Add permissions** drop down, and select **Attach policies**.
![\[AWS SageMaker AI execution role management interface showing summary details, permissions policies, and options to add or attach policies.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/execution-role-management-interface.png)
1. Attach the policy
On the **Attach policy to AmazonSageMaker AI-ExecutionRole-** page, in the **Other permissions policies** section search bar, enter **AmazonAmazon TextractFullAccess**. Then, select the **policy**, and choose **Add permissions**.
![\[AWS console showing the attachment of the "AmazonAmazon TextractFullAccess" policy to an Amazon SageMaker AI execution role, with the policy selected and the "Add permissions" button highlighted.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/console-attachment-textractlong-fullaccess.png)
1. Run the notebook
Navigate back to your **JupyterLab** tab, and select **Run**.
![\[A Jupyter Notebook interface showing Python code for processing image files, including functions to list image files, check if a file should be processed, and extract text using Amazon Textract.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/jupyter-notebook-interface-code-processing.png)
1. View the text file
After your code runs you should now be able to see a ****.txt file**** with the extracted text in the left navigation pane of your JupyterLab.
![\[A file explorer and text editor showing a health insurance card's redacted details, including member name, ID, plan type, and coverage information.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/file-explorer-text-editor-health-insurance.png)
### Clean up resources
In this step, you will go through the steps to delete all the resources you created throughout this tutorial. It is recommended that you stop the Jupyter notebook you created to prevent unexpected costs.
+ Delete the notebook
In the SageMaker AI console, in the left navigation pane, choose **Notebooks**, and select the **Notebook**. Then, choose **Actions**, and select **Stop**.
**Note**
The stop operation might take around 5 minutes. Once the notebook is stopped you can also delete it by choosing **Actions** and selecting **Delete**.
![\[The navigation menu interface.\]](http://docs.aws.amazon.com/hands-on/latest/extract-text-with-amazon-textract/images/navigation-menu-interface.png)
## Congratulations
You have created a sample Proof-of-Concept to extract information from documents.