Shows how to use the AWS SDK for Python (Boto3) in a Jupyter notebook to detect entities in text that is extracted from an image. This example uses Amazon Textract to extract text from an image stored in Amazon Simple Storage Service (Amazon S3) and Amazon Comprehend to detect entities in the extracted text.
- As an AWS best practice, grant this code least privilege, or only the permissions required to perform a task. For more information, see Grant Least Privilege in the AWS Identity and Access Management User Guide.
- This code has not been tested in all AWS Regions. Some AWS services are available only in specific Regions. For more information, see the AWS Region Table on the AWS website.
- Running this code might result in charges to your AWS account.
- You must have an AWS account, and have your default credentials and AWS Region configured as described in the AWS Tools and SDKs Shared Configuration and Credentials Reference Guide.
- Python 3.5 or later
- Boto3 1.17.96 or later
- Pillow 8.1.1 or later
- Pandas 1.3.2 or later
For this example, you need the following resources:
- An Amazon S3 bucket.
- An image in the Amazon S3 bucket containing text.
Create these resources by using the AWS Management Console or the AWS SDK for Python.
This example is a Jupyter notebook and must be run in an environment that can host notebooks. For instructions on how to run the example using Amazon SageMaker, see the directions in TextractAndComprehendNotebook.ipynb.
After running the example, remember to delete any resources you are no longer using, including your Amazon S3 bucket.
To run the unit tests:
- Install the Testbook library.
- Replace the values of the indicated variables, specified in the
test-textract-comprehend.pyPython file, with the values that you want to use. - Run the test file at a command prompt:
python test-textract-comprehend.py.
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