AWS Public Sector Blog

Transforming government application systems using intelligent document processing on AWS

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Government agencies handle vast volumes of bureaucratic documents daily, ranging from tax forms to medical records. This document-heavy workflow, often reliant on manual processing, can result in delays, errors, and increased operational inefficiencies, causing frustration among both employees and stakeholders. For example, incorrect data entry or misplaced documents can significantly slow down the approval of important applications, leading to citizen dissatisfaction and potential compliance issues. There is a growing need for a solution that automates document extraction, analysis, and classification while maintaining high accuracy and reliability.

By integrating human review into the artificial intelligence and machine learning (AI/ML) workflow, government agencies maintain quality control. Personalized, automated notifications keep all stakeholders informed in real-time.

This post explores how intelligent document processing (IDP) solutions from Amazon Web Services (AWS) can modernize bureaucratic workflows, improve efficiency, and enhance service delivery within government agencies.

Drowning in paperwork: The challenges of document processing in government

Government agencies are no strangers to the overwhelming burden of paperwork. From tax forms and medical records to permit applications and licensing documents, the sheer volume of bureaucratic materials processed by these organizations daily is overwhelming.

Unfortunately, the reliance on manual, paper-based processes to handle this document-heavy workflow has led to a range of impactful challenges that undermine efficiency, frustrate both employees and citizens, and expose agencies to significant risk.

Examples of the challenges impacting workflows include the following:

  • Slow processing times – One of the most pressing issues is the extremely slow pace of document processing. With manual data entry and physical file handling, basic applications can languish for weeks, or potentially months, leaving citizens waiting and agency staff struggling to keep up with the backlog.
  • Data entry errors – Transcribing information from physical documents into digital systems introduces the risk of human error, and these mistakes can have serious consequences. Inaccurate data can lead to rejected applications, delayed decisions, and compliance issues—problems that undermine public trust and create headaches for everyone involved.
  • Lack of visibility and transparency – Without automated tracking and reporting, it’s difficult for government agencies to maintain visibility into the status of documents and applications. This lack of transparency makes it challenging to monitor workflows, identify bottlenecks, and maintain accountability.
  • Inefficient storage and retrieval – Physical document storage and manual filing systems can turn even the most straightforward document search into a frustrating challenge. When critical files are misplaced or lost, productivity halts.
  • Compliance and security risks – The reliance on paper-based processes increases the risk of sensitive information being lost, stolen, or accessed by unauthorized individuals. This increases the risk of data breaches and puts agencies in jeopardy of violating strict compliance regulations.
  • High operational costs – The human labor and physical infrastructure needed to maintain manual document processing workflows are resource-intensive and expensive, siphoning away funds that could be better invested in serving citizens.
  • Scaling challenges – As the volume of documents continues to grow, manual processes become increasingly strained, unable to keep up with spikes in workload. This leads to mounting backlogs, service disruptions, and frustrated citizens.

Government agencies can no longer afford to be bogged down by these serious challenges. Let us explore solutions that can automate and streamline document-heavy workflows, freeing up resources and allowing agencies to deliver the efficient, transparent, and citizen-centric services their communities deserve.

Transforming government application systems using IDP on AWS

The workflow begins with users uploading documents, which triggers a sequence of AWS services, each responsible for specific tasks, such as text extraction, classification, confidence scoring, and human review, if necessary. Real-time notifications keep the process transparent, while a human in the loop strategy makes sure that any low-confidence results are accurately handled, balancing automation with human oversight.

Figure 1. Architectural diagram of the solution described in this post, using Amazon Textract, Amazon Comprehend, and AWS Lambda.

To start the workflow, users upload documents into Amazon Simple Storage Service (Amazon S3), a scalable storage service that stores documents securely and triggers the next steps in the workflow. As soon as the document is uploaded to Amazon S3, it triggers a notification to Amazon Simple Queue Service (Amazon SQS). 

Amazon SQS is responsible for queuing the document for processing. This makes sure that each document is processed in the order it was uploaded, even under heavy loads, allowing the system to scale efficiently.

The document is now ready for processing, and AWS Lambda is automatically invoked to handle the next step. Lambda is a serverless compute service that pulls the document from the queue and sends it to Amazon Textract, an ML service that automatically extracts text, handwriting, and data from documents, including complex formats, such as forms and tables. It’s much more advanced than traditional optical character recognition (OCR), as it understands the structure and relationships of the text.

When Amazon Textract has completed the text extraction process, a completion event is sent to Amazon Simple Notification Service (SNS). This service makes sure that notifications are sent to the relevant components in the system, informing them that text extraction is complete. SNS plays a key role in coordinating asynchronous tasks and triggering further actions in the workflow.

The extracted text is processed by another Lambda function, which forwards it to a custom classifier in Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that can classify the content of documents based on trained models. This can be particularly useful for identifying the type or category of the document, or for tagging it with relevant metadata.

When Amazon Comprehend classifies the document, the results are stored in Amazon S3 and a final Lambda function is invoked to check the confidence score of the classification.

Based on the confidence score of the document classification, the workflow is impacted in one of the following two ways:

  • If the confidence score is high, the document is considered correctly processed and stored in Amazon DynamoDB—a NoSQL database—for future retrieval and analysis.
  • If the confidence score is low, the document is flagged for human review using Amazon Augmented AI (A2I).

For documents with lower confidence scores, Amazon A2I introduces human reviewers into the process. Human reviewers are tasked with reviewing and correcting any issues in document classification, which maintains higher accuracy. When the human review is complete, the updated results are stored in Amazon S3 for further processing.

For documents that have significant issues, such as those with especially low confidence scores, the system automatically triggers an email notification using Amazon Simple Email Service (Amazon SES). This sends personalized notifications to the relevant stakeholders, such as document submitters or administrators, making sure they are kept up to date on the status of the document.

When all processing is complete—whether through AI models or human intervention—the results are stored in Amazon DynamoDB, a scalable NoSQL database used for storing classified document data, allowing for quick access and retrieval when it’s needed.

If a human reviewer updates the document classification or processing results, another Lambda function is invoked to update the final document record in Amazon DynamoDB.

Unlocking the full potential of government paperwork

Through this end-to-end IDP workflow powered by AWS, government agencies can finally overcome the severe challenges associated with manual document management. By automating the core tasks of text extraction, classification, and quality control, agencies can significantly streamline their bureaucratic processes, resulting in faster application approvals, reduced errors, and enhanced transparency for both employees and citizens.

The integration of human review into the AI-driven workflow assures that critical decisions are always verified and validated, balancing the speed and scalability of automation with the nuanced judgment of subject matter experts. Real-time notifications and status updates further enhance collaboration and keep all stakeholders informed, fostering greater trust and accountability.

Moreover, the centralized storage and retrieval of processed documents in Amazon DynamoDB provides new opportunities for data-driven insights and strategic decision-making. Agencies can use the wealth of information gathered through this IDP system to identify patterns, optimize workflows, and continuously improve their service delivery.

By embracing the power of the comprehensive IDP solutions provided by AWS, government agencies can overcome the constraints of manual bureaucracy and transform their document-heavy operations into a well-developed, citizen-centric process. The future of efficient, transparent, and responsive government services is here—and it’s powered by intelligent document processing.

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