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Streamline Document Processing with Amazon A2I and Amazon Extract

Introduction
Challenges in Document Processing
Traditional Methods of Extracting Data from Documents
Introduction to Amazon A2I and Amazon Extract
Benefits of Using Amazon A2I
How Amazon A2I Works with Amazon Extract
Steps to Get Started with Amazon A2I
Uploading Data to Amazon S3 Bucket
Invoking the Human Review Loop with Amazon Extract
Logging into the A2I Console and Validating the Output
Conclusion
Resources

Introduction

In today's digital age, documents play a crucial role in businesses as they contain valuable information that can impact operations. Extracting data from documents is essential to automate document processing workflows. However, organizations face several challenges in this process, including manual extraction, rule-based systems, and machine learning infrastructure and skills requirements. To address these challenges, AWS offers various services, including Amazon A2I and Amazon Extract. This article will explore how you can leverage Amazon A2I with existing document processing workflows and the benefits it provides.

Challenges in Document Processing

One of the primary challenges in document processing is manual extraction, which is time-consuming, error-prone, and expensive. Another approach is using rule-based systems or OCR-based systems. However, these systems lack intelligence and struggle with format changes. Machine learning can also be used to extract data from documents, but it requires significant infrastructure and expertise. Additionally, a human review workflow is often needed to ensure accurate results.

Traditional Methods of Extracting Data from Documents

Before diving into Amazon A2I, let's briefly discuss the traditional methods of extracting data from documents. Manual extraction involves manually copying information from documents, which is labor-intensive and prone to errors. Rule-based systems use predefined rules to extract data, but they are not adaptable to format changes. OCR-based systems leverage optical character recognition technology to extract text from documents, but they also have limitations in handling unstructured data.

Introduction to Amazon A2I and Amazon Extract

Amazon A2I (Augmented AI) is a service provided by AWS that allows you to easily implement human review workflows. It offers pre-built templates and simplifies the management of reviewers. Amazon Extract is an AI service that helps extract data from any document at any Scale. It eliminates the need for extensive machine learning expertise, as it is built upon easy-to-use APIs.

Benefits of Using Amazon A2I

Using Amazon A2I provides several benefits for document processing workflows:

Easily implement human review workflows: Amazon A2I comes with over 70 pre-built templates that allow you to quickly set up human review processes. These templates cover a wide range of use cases, making it easier to get started.
Reduce time to market: By leveraging a human review loop, low confidence predictions can be caught and rectified. This ensures that only accurate data is processed, reducing the time required to market your applications.
Flexible workforce options: Amazon A2I supports multiple workforce options. You can use Amazon Mechanical Turk, onboard your own employees, or choose a private workforce option for sensitive data. This flexibility allows you to adapt to your specific requirements.
Integrates with custom machine learning models: Amazon A2I not only integrates with Amazon Extract but also with other machine learning models like Amazon Comprehend or Amazon Recognition. You can easily set up a human in the loop process for any model trained using AWS SageMaker.

How Amazon A2I Works with Amazon Extract

Document Scanning: The process begins by scanning the documents, which can be in PDF or image format.
Amazon Extract: Amazon Extract is used to extract text, key-value pairs, and tables from the scanned documents. It provides a confidence score for each extracted element.
Setting up Human Review Workflow: With Amazon A2I, you can define a threshold for triggering a human review loop. For example, you can set a threshold of 90% confidence and above to directly send the data to your client application. Anything below this threshold is marked as low confidence and sent for a human review.
Human Review Loop: Multiple reviewers can log into the Amazon A2I console and review the documents with low confidence elements. The results of the human review are automatically saved in your Amazon S3 bucket.
Consumption by Client Application: The reviewed and corrected data is then consumed directly by your client application, streamlining the document processing workflow.

Steps to Get Started with Amazon A2I

To quickly get started with Amazon A2I, follow these five steps:

Create a Private Workforce: Begin by creating a private workforce in the Amazon A2I console. You can either create a new Amazon Cognito user group or import your existing user groups. Additionally, you can set up SNS topic notifications for your work team.
Set up Human Review Workflows: Create a human review workflow in the Amazon A2I console. Specify the name, an Amazon S3 bucket to store the output, and an IAM role. You can choose the task type, such as key-value pair extraction, and configure when the human review should be invoked based on confidence scores or missing form keys.
Worker Tasks Template: You can use the default template provided by Amazon A2I or create your own template. Specify instructions for the workers and choose the private work team created in step one.
Upload Data to Amazon S3 Bucket: Upload the documents that need processing to an Amazon S3 bucket. Ensure that the Cross-Origin Resource Sharing configuration is enabled.
Invoke the Human Review Loop: Use CLI commands or Python APIs to invoke the human review workflow with the Amazon Extract flow definition and the name of the S3 bucket. This will initiate the human review process.

Uploading Data to Amazon S3 Bucket

Before invoking the human review loop, the data that requires processing needs to be uploaded to an Amazon S3 bucket. Make sure the necessary permissions are set and that the bucket supports Cross-Origin Resource Sharing (CORS) configuration. This step ensures seamless data transfer during the workflow.

Invoking the Human Review Loop with Amazon Extract

There are two ways to invoke the human review loop with Amazon Extract:

CLI Command: Use CLI commands by creating a JSON file that specifies the flow definition ARN and the name of the S3 bucket where the document is stored. Running the CLI command will create an A2I loop for the document.
Python APIs: Alternatively, you can use Python APIs to set up a human loop configuration. This involves defining the ARN, giving the human loop a unique name, and calling the text.analyze_document API with the human loop configuration. This will configure the group for human review.

Logging into the A2I Console and Validating the Output

Once the human review loop is set up, reviewers can log into the Amazon A2I console and start working on the documents. They can validate the extracted data, correct any errors, and submit the reviewed output. The corrected data will be automatically saved in the Amazon S3 bucket and can be directly consumed by the client application.

Conclusion

Amazon A2I, integrated with Amazon Extract, provides a powerful solution for automating document processing workflows. It eliminates the need for extensive machine learning expertise and allows for the easy implementation of human review loops. By leveraging these services, organizations can significantly reduce the time required for document processing and ensure accurate results.