In this blog post, we’ll explore how to build an intelligent receipt reader using AWS Textract and AWS Comprehend. This solution will automate the extraction and analysis of data from receipts, making it easier to manage and utilize the information they contain. We’ll cover the requirements, setup, and detailed steps to implement this project.

Requirements and Setup

Before we dive into the project, ensure you have the following prerequisites:

  • An AWS account with administrative privileges.
  • Basic knowledge of AWS services (Textract, Comprehend, S3, IAM, Lambda).
  • Python programming experience.
  • AWS CLI is installed and configured on your local machine.

Project Summary

This project involves the following steps:

  1. Setting up S3 buckets for storing receipts and outputs.
  2. Configuring IAM roles and policies to secure access to resources.
  3. Using AWS Textract to extract text from receipts.
  4. Leveraging AWS Comprehend to analyze and categorize extracted data.
  5. Deploying a Lambda function to automate the workflow.
  6. Integrating triggers and setting up CloudWatch for monitoring.

Exploring AWS Textract’s OCR Capabilities

AWS Textract is a powerful service automatically extracts text, forms, and tables from scanned documents. It uses machine learning to read and process various document formats, making it ideal for our receipt reader.

Steps:

  1. Create an S3 bucket to store the receipts.
  2. Upload a sample receipt to the bucket.
  3. Invoke Textract API to analyze the receipt and extract text.
  4. Review the output to understand the extracted data structure.

Understanding AWS Comprehend

AWS Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. It helps to categorize and understand the extracted data from receipts.

Steps:

  1. Extract entities like dates, prices, and merchant names from the text output by Textract.
  2. Classify the text to understand categories such as expenses, payments, and others.
  3. Analyze sentiment, if required, to gain insights into customer feedback.

Configuring S3 Storage

S3 buckets are essential for storing the input receipts and the processed outputs.

Establishing the Receipt Upload Bucket:

  1. Create an S3 bucket named receipt-upload-bucket.
  2. Set appropriate permissions to allow uploading receipts.

Creating the Output Bucket:

  1. Create another S3 bucket named receipt-output-bucket.
  2. Configure access policies to allow writing processed data.

Setting Up IAM Roles

IAM roles are crucial for managing access to AWS resources securely.

Defining Custom Policies:

  1. Create a policy that grants Textract permission to read from receipt-upload-bucket.
  2. Create a policy that allows Comprehend to process data.
  3. Define a policy for Lambda to read from receipt-upload-bucket and write to receipt-output-bucket.

Role Creation Process:

  1. Create an IAM role for Textract with the custom policy attached.
  2. Create another IAM role for Lambda with the necessary policies attached.

Deploying the Lambda Function

AWS Lambda will automate the receipt processing workflow.

Steps:

  1. Write a Lambda function in Python to invoke Textract and Comprehend.
  2. Configure the function to trigger receipt upload events in receipt-upload-bucket.
  3. Set up environment variables to store configuration details.

Integrating Triggers

Integrate S3 triggers to automate the invocation of the Lambda function whenever a new receipt is uploaded.

Steps:

  1. Navigate to the S3 bucket and configure event notifications.
  2. Set the event type to ObjectCreated.
  3. Link the event to trigger the Lambda function.

Configuring CloudWatch Logs

Monitor and debug the solution using CloudWatch logs.

Steps:

  1. Enable CloudWatch logging for the Lambda function.
  2. Set log retention policies as per your requirements.
  3. Review logs to ensure the solution is functioning correctly.

Service Testing and Validation

Finally, the entire setup will be tested to validate the solution.

Steps:

  1. Upload sample receipts to the receipt-upload-bucket.
  2. Monitor the Lambda function to ensure it processes the receipts.
  3. Check the receipt-output-bucket for the extracted and analyzed data.
  4. Review CloudWatch logs for any errors and troubleshoot if necessary.

References

Build a receipt and invoice processing pipeline with Amazon Textract

Announcing specialized support for extracting data from invoices and receipts using Amazon Textract