aws-solutions-library-samples/accelerated-intelligent-document-processing-on-aws

The IDP Accelerator provides a scalable, serverless approach for automated document processing and information extraction using AWS services, such as Amazon Bedrock Data Automation and Amazon Bedrock foundational models. It combines generative AI and optical character recognition (OCR) to process documents at scale.

What it solves

This project provides a scalable, serverless system for automated document processing. It solves the challenge of converting large volumes of unstructured documents into structured data by combining Optical Character Recognition (OCR) with generative AI, reducing the need for manual data entry and extraction.

How it works

Built on AWS serverless technologies (Lambda, Step Functions, SQS, DynamoDB), the system offers two primary processing modes:

  1. Pipeline Mode: Follows a sequence of OCR, Bedrock-based classification (page-level or holistic), Bedrock-based extraction, confidence assessment, rule validation, and summarization.
  2. BDA Mode: Utilizes Bedrock Data Automation (BDA) for end-to-end processing, followed by rule validation and summarization.

Users can interact with the system via a Web UI, direct S3 uploads, or a dedicated Command Line Interface (CLI) for batch processing and evaluation.

Who it’s for

It is designed for organizations needing to process high volumes of documents at scale, developers looking for a modular framework to implement intelligent document processing (IDP), and businesses requiring human-in-the-loop validation for critical data extraction.

Highlights

  • Serverless Architecture: Fully managed AWS infrastructure for high throughput and cost optimization.
  • Flexible Processing: Supports both a modular pipeline and an end-to-end BDA mode.
  • AI-Powered Quality Control: Includes LLM-powered extraction confidence assessments and an AI-powered evaluation framework to measure accuracy against baselines.
  • Human-in-the-Loop (HITL): Built-in review system for human validation of extracted data.
  • Extensibility: Supports few-shot example prompting, custom business logic via Lambda, and Model Context Protocol (MCP) integration for external analytics tools.
  • Comprehensive Tooling: Includes a Web UI for monitoring, a CLI for programmatic batch runs, and a knowledge base for querying processed documents.

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