Build end-to-end document processing pipelines with Amazon Textract IDP CDK Constructs

Intelligent document processing (IDP) with AWS helps automate information extraction from documents of different types and formats, quickly and with high accuracy, without the need for machine learning (ML) skills. Faster information extraction with high accuracy can help you make quality business decisions on time, while reducing overall costs. For more information, refer to Intelligent document processing with AWS AI services: Part 1.

However, complexity arises when implementing real-world scenarios. Documents are often sent out of order, or they may be sent as a combined package with multiple form types. Orchestration pipelines need to be created to introduce business logic, and also account for different processing techniques depending on the type of form inputted. These challenges are only magnified as teams deal with large document volumes.

In this post, we demonstrate how to solve these challenges using Amazon Textract IDP CDK Constructs, a set of pre-built IDP constructs, to accelerate the development of real-world document processing pipelines. For our use case, we process an Acord insurance document to enable straight-through processing, but you can extend this solution to any use case, which we discuss later in the post.

Acord document processing at scale

Straight-through processing (STP) is a term used in the financial industry to describe the automation of a transaction from start to finish without the need for manual intervention. The insurance industry uses STP to streamline the underwriting and claims process. This involves the automatic extraction of data from insurance documents such as applications, policy documents, and claims forms. Implementing STP can be challenging due to the large amount of data and the variety of document formats involved. Insurance documents are inherently varied. Traditionally, this process involves manually reviewing each document and entering the data into a system, which is time-consuming and prone to errors. This manual approach is not only inefficient but can also lead to errors that can have a significant impact on the underwriting and claims process. This is where IDP on AWS comes in.

To achieve a more efficient and accurate workflow, insurance companies can integrate IDP on AWS into the underwriting and claims process. With Amazon Textract and Amazon Comprehend, insurers can read handwriting and different form formats, making it easier to extract information from various types of insurance documents. By implementing IDP on AWS into the process, STP becomes easier to achieve, reducing the need for manual intervention and speeding up the overall process.

This pipeline allows insurance carriers to easily and efficiently process their commercial insurance transactions, reducing the need for manual intervention and improving the overall customer experience. We demonstrate how to use Amazon Textract and Amazon Comprehend to automatically extract data from commercial insurance documents, such as Acord 140, Acord 125, Affidavit of Home Ownership, and Acord 126, and analyze the extracted data to facilitate the underwriting process. These services can help insurance carriers improve the accuracy and speed of their STP processes, ultimately providing a better experience for their customers.

Solution overview

The solution is built using the AWS Cloud Development Kit (AWS CDK), and consists of Amazon Comprehend for document classification, Amazon Textract for document extraction, Amazon DynamoDB for storage, AWS Lambda for application logic, and AWS Step Functions for workflow pipeline orchestration.

The pipeline consists of the following phases:

  1. Split the document packages and classification of each form type using Amazon Comprehend.
  2. Run the processing pipelines for each form type or page of form with the appropriate Amazon Textract API (Signature Detection, Table Extraction, Forms Extraction, or Queries).
  3. Perform postprocessing of the Amazon Textract output into machine-readable format.

The following screenshot of the Step Functions workflow illustrates the pipeline.


To get started with the solution, ensure you have the following:

  • AWS CDK version 2 installed
  • Docker installed and running on your machine
  • Appropriate access to Step Functions, DynamoDB, Lambda, Amazon Simple Queue Service (Amazon SQS), Amazon Textract, and Amazon Comprehend

Clone the GitHub repo

Start by cloning the GitHub repository:

git clone

Create an Amazon Comprehend classification endpoint

We first need to provide an Amazon Comprehend classification endpoint.

For this post, the endpoint detects the following document classes (ensure naming is consistent):

  • acord125
  • acord126
  • acord140
  • property_affidavit

You can create one by using the comprehend_acord_dataset.csv sample dataset in the GitHub repository. To train and create a custom classification endpoint using the sample dataset provided, follow the instructions in Train custom classifiers. If you would like to use your own PDF files, refer to the first workflow in the post Intelligently split multi-form document packages with Amazon Textract and Amazon Comprehend.

After training your classifier and creating an endpoint, you should have an Amazon Comprehend custom classification endpoint ARN that looks like the following code:


Navigate to docsplitter/ and modify lines 27–28, which contain comprehend_classifier_endpoint. Enter your endpoint ARN in line 28.

Install dependencies

Now you install the project dependencies:

python -m pip install -r requirements.txt

Initialize the account and Region for the AWS CDK. This will create the Amazon Simple Storage Service (Amazon S3) buckets and roles for the AWS CDK tool to store artifacts and be able to deploy infrastructure. See the following code:

cdk bootstrap

Deploy the AWS CDK stack

When the Amazon Comprehend classifier and document configuration table are ready, deploy the stack using the following code:

cdk deploy DocumentSplitterWorkflow --outputs-file document_splitter_outputs.json --require-approval never

Upload the document

Verify that the stack is fully deployed.

Then in the terminal window, run the aws s3 cp command to upload the document to the DocumentUploadLocation for the DocumentSplitterWorkflow:

aws s3 cp sample-doc.pdf $(aws cloudformation list-exports --query 'Exports[?Name==`DocumentSplitterWorkflow-DocumentUploadLocation`].Value' --output text)

We have created a sample 12-page document package that contains the Acord 125, Acord 126, Acord 140, and Property Affidavit forms. The following images show a 1-page excerpt from each document.

All data in the forms is synthetic, and the Acord standard forms are the property of the Acord Corporation, and are used here for demonstration only.

Run the Step Functions workflow

Now open the Step Function workflow. You can get the Step Function workflow link from the document_splitter_outputs.json file, the Step Functions console, or by using the following command:

aws cloudformation list-exports --query 'Exports[?Name==`DocumentSplitterWorkflow-StepFunctionFlowLink`].Value' --output text

Depending on the size of the document package, the workflow time will vary. The sample document should take 1–2 minutes to process. The following diagram illustrates the Step Functions workflow.

When your job is complete, navigate to the input and output code. From here you will see the machine-readable CSV files for each of the respective forms.

To download these files, open Set files to be the list outputted by the state machine run. You can run this function by running python3 This will generate the csvfiles_ folder, as shown in the following screenshot.

Congratulations, you have now implemented an end-to-end processing workflow for a commercial insurance application.

Extend the solution for any type of form

In this post, we demonstrated how we could use the Amazon Textract IDP CDK Constructs for a commercial insurance use case. However, you can extend these constructs for any form type. To do this, we first retrain our Amazon Comprehend classifier to account for the new form type, and adjust the code as we did earlier.

For each of the form types you trained, we must specify its queries and textract_features in the file. This customizes each form type’s processing pipeline by using the appropriate Amazon Textract API.

Queries is a list of queries. For example, “What is the primary email address?” on page 2 of the sample document. For more information, see Queries.

textract_features is a list of the Amazon Textract features you want to extract from the document. It can be TABLES, FORMS, QUERIES, or SIGNATURES. For more information, see FeatureTypes.

Navigate to Each document type needs its classification, queries, and textract_features configured by creating CSVRow instances.

For our example we have four document types: acord125, acord126, acord140, and property_affidavit. In in the following we want to use the FORMS and TABLES features on the acord documents, and the QUERIES and SIGNATURES features for the property affidavit.

def get_csv_rows():
# acord125
acord125_queries: List[List[str]] = list()
acord_125_features: List[str] = ["FORMS", "TABLES"]
acord125_row = CSVRow("acord125",
# acord126
acord126_queries: List[List[str]] = list()
acord126_features: List[str] = ["FORMS", "TABLES"]
acord126_row = CSVRow("acord126",
# acord140
acord140_queries: List[List[str]] = list()
acord140_features: List[str] = ["FORMS", "TABLES"]
acord140_row = CSVRow("acord140",
# property_affidavit
property_affidavit_queries: List[List[str]] = [
["PROP_AFF_OWNER", "What is your name?"],
["PROP_AFF_ADDR", "What is the property's address?"],
["PROP_AFF_DATE_EXEC_ON", "When was this executed on?"],
["PROP_AFF_DATE_SWORN", "When was this subscribed and sworn to?"],
["PROP_AFF_NOTARY", "Who is the notary public?"],
property_affidavit_features: List[str] = ["SIGNATURES", "QUERIES"]
property_affidavit_row = CSVRow("property_affidavit",

Refer to the GitHub repository for how this was done for the sample commercial insurance documents.

Clean up

To remove the solution, run the cdk destroy command. You will then be prompted to confirm the deletion of the workflow. Deleting the workflow will delete all the generated resources.


In this post, we demonstrated how you can get started with Amazon Textract IDP CDK Constructs by implementing a straight-through processing scenario for a set of commercial Acord forms. We also demonstrated how you can extend the solution to any form type with simple configuration changes. We encourage you to try the solution with your respective documents. Please raise a pull request to the GitHub repo for any feature requests you may have. To learn more about IDP on AWS, refer to our documentation.

About the Authors

Raj Pathak is a Senior Solutions Architect and Technologist specializing in Financial Services (Insurance, Banking, Capital Markets) and Machine Learning. He specializes in Natural Language Processing (NLP), Large Language Models (LLM) and Machine Learning infrastructure and operations projects (MLOps).

Aditi Rajnish is a Second-year software engineering student at University of Waterloo. Her interests include computer vision, natural language processing, and edge computing. She is also passionate about community-based STEM outreach and advocacy. In her spare time, she can be found rock climbing, playing the piano, or learning how to bake the perfect scone.

Enzo Staton is a Solutions Architect with a passion for working with companies to increase their cloud knowledge. He works closely as a trusted advisor and industry specialist with customers around the country.

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