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Introduction
Generative artificial intelligence (AI) is not only empowering innovation through ideation, content creation, and enhanced customer service but also streamlining operations and boosting productivity across various domains. To effectively harness this transformative technology, Amazon Bedrock offers a fully managed service that integrates high-performing foundation models (FMs) from leading AI companies, such as AI21 Labs, Anthropic, Cohere, Meta, Stability AI, Mistral AI, and Amazon.
By providing access to these advanced models through a single API and supporting the development of generative AI applications with an emphasis on security, privacy, and responsible AI, Amazon Bedrock enables you to use AI to explore new avenues for innovation and improve overall offerings.
Solution Overview
The proposed solution uses Amazon Bedrock and the powerful Anthropic Claude 3 Sonnet model to enable IDP capabilities. The architecture consists of several AWS services seamlessly integrated with the Amazon Bedrock, enabling efficient and accurate extraction of data from scanned documents.
The following diagram illustrates our solution architecture.
The solution consists of the following steps:
- The process begins with scanned documents being uploaded and stored in an Amazon Simple Storage Service (Amazon S3) bucket, which invokes an S3 Event Notification on object upload.
- This event invokes an AWS Lambda function, responsible for invoking the Anthropic Claude 3 Sonnet model on Amazon Bedrock.
- The Anthropic Claude 3 Sonnet model, with its advanced multimodal capabilities, processes the scanned documents and extracts relevant data in a structured JSON format.
- The extracted data from the Anthropic Claude 3 Sonnet model is sent to an Amazon Simple Queue Service (Amazon SQS) queue. Amazon SQS acts as a buffer, allowing components to send and receive messages reliably without being directly coupled, providing scalability and fault tolerance in the system.
- Another Lambda function consumes the messages from the SQS queue, parses the JSON data, and stores the extracted key-value pairs in an Amazon DynamoDB table for retrieval and further processing.
Prerequisites
You need the following prerequisites before you can proceed with this solution. For this post, we use the us-east-1 AWS Region. For details on available Regions, see Amazon Bedrock endpoints and quotas.
Use Case and Dataset
For our example use case, let’s look at a state agency responsible for issuing birth certificates. The agency may receive birth certificate applications through various methods, such as online applications, forms completed at a physical location, and mailed-in completed paper applications. Today, most agencies spend a considerable amount of time and resources to manually extract the application details. The process begins with scanning the application forms, manually extracting the details, and then entering them into an application that eventually stores the data into a database. This process is time-consuming, inefficient, not scalable, and error-prone. Additionally, it adds complexity if the application form is in a different language (such as Spanish).
For this demonstration, we use sample scanned images of birth certificate application forms. These forms don’t contain any real personal data. Two examples are provided: one in English (handwritten) and another in Spanish (printed). Save these images as.jpeg files to your computer. You need them later for testing the solution.
Test the Solution
Now that you have created all the necessary resources, permissions, and code, it’s time to test the solution.
In the S3 folder birth_certificates, upload the two scanned images that you downloaded earlier. Then open the DynamoDB console and explore the items in the birth_certificates table.
Conclusion
In this post, we demonstrated how to use Amazon Bedrock and the powerful Anthropic Claude 3 Sonnet model to develop an IDP solution. By harnessing the advanced multimodal capabilities of Anthropic Claude 3, we were able to accurately extract data from scanned documents and store it in a structured format in a DynamoDB table.
Frequently Asked Questions
Q1: What is the purpose of this article?
This article demonstrates how to use Amazon Bedrock and the powerful Anthropic Claude 3 Sonnet model to develop an IDP solution, enabling organizations to streamline their document processing workflows and extract valuable insights.
Q2: What is the architecture of the solution?
The solution architecture consists of several AWS services seamlessly integrated with the Amazon Bedrock, enabling efficient and accurate extraction of data from scanned documents.
Q3: What is the Anthropic Claude 3 Sonnet model?
The Anthropic Claude 3 Sonnet model is a powerful AI model that is optimized for speed and efficiency, making it an excellent choice for intelligent tasks – particularly for enterprise workloads. It also possesses sophisticated vision capabilities, demonstrating a strong aptitude for understanding a wide range of visual formats, including photos, charts, graphs, and technical diagrams.
Q4: How does the solution handle multiple languages?
The solution is capable of handling multiple languages, including English and Spanish. The Anthropic Claude 3 Sonnet model is trained on a large dataset of text and can accurately extract data from documents written in different languages.
Q5: What are the benefits of using this solution?
By using this solution, organizations can streamline their document processing workflows, extract valuable insights, and enhance operational efficiency. Additionally, the solution provides accurate and reliable data extraction, reducing the risk of human error and increasing the accuracy of downstream processing.
