Automate data processing from documents

Improve employee productivity and make faster decisions with intelligent document processing

Documents come in various file types, varied formats, and contain valuable information. In most cases, you are manually processing the documents which is time consuming, prone to error, and costly. Not only do you want this information quickly but likely need to use the information within those documents for downstream applications.

To help overcome these challenges, AWS Machine Learning (ML) now provides you choices when it comes to extracting information from complex content in any document format such as insurance claims, mortgages, healthcare claims, contracts, and legal contracts. 

Automate Data Extraction and Analysis from Documents with Machine Learning (2:41)



Higher accuracy of data

Using ML can help you process documents faster and more accurately, reducing errors caused from manual entry. In cases where data needs to be 100% accurate, a human step in at any time and review data.


Faster data processing

Implementing intelligent document processing can help you accomplish weeks or months of work in a matter of days.


Improved employee productivity

Machine learning removes the manual process of pulling out insights from documents and entering information into various systems, enabling your employees to spend more time on value-adding business tasks.


Cost savings

Automating document workflows reduces the complexity of data extraction and
analysis, reducing the average cost per document.



Mortgage packets come with varying document types such as tax filings, W-2’s, paystubs, and applications which often times need to be split and classified. Using ML you can extract the most important information out of mortgage applications such as asset valuation, credit score or property value using a combination of Optical Character Recognition (OCR) and Natural Language Processing (NLP) to speed up response times to your customers. 

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Many insurance forms have varied layouts and formats which makes text extraction difficult. Using machine learning, you can extract relevant fields such as estimate for repairs, property address or case ID from sections of a document or classify documents with ease. By combining text extraction and NLP, you can process insurance forms such as insurance quotes, binders, ACORD forms, and claims forms faster, with higher accuracy. 

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Capital Markets

Financial proxy statements, SEC filings (10-K, 8-K, 14A, 497K, etc.), KYC forms, tax documents and more come in dense text format or mixed with tables and text making it difficult to process using traditional methods. Using AWS ML, you can process various formats and file types using OCR and NLP combined to extract table formats and derive entities from documents and use custom models to recognize the entities and classify documents. 

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Many healthcare forms have free form text, dense paragraphs, checkboxes, and tables. Using ML, extract the necessary data out of these documents regardless of layout. By using the forms and tables extraction API and Natural Language Processing, you can not only leverage text extraction but also extract medical terminology from medical forms to provide fast results to your patients and subscribers. 

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Bill of materials (BOM), Certificates of Analysis (COA), and Purchase Orders (PO) are a major part of a manufacturing operations, which today is usually manual and time consuming. Using AI, you can now automate the process by extracting text from contracts, identifying specific fields and values, and use the data to inform downstream systems in your manufacturing systems. 

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Oil & Gas

Your organization has been keeping records for centuries, those documents contain valuable information about various parts of your business operations such as pressure test records or maintenance records. The documents have a mix of text and images which makes building a document pipeline a challenge. Using computer vision, you can build a custom pipeline to extract the text from the documents as well as the diagrams or images on the pages to aid in overcoming the manual process of reviewing these documents one by one. 

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Processing documents, such as agreements, court filings, or legal dockets, is a difficult task for legal teams. Contractual documents are often in non-standardized formats. The typical workflow for reviewing legal filings involves loading, reading, and extracting case number, parties involved or legal entities from the documents, requiring hours of manual effort. Using OCR and NLP to extract text and specific terms can automate this process, with higher accuracy. 

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Accounts Payable

Invoices and receipts are vital to all organizations and many times those types of documents come in various layouts. Using ML you can automatically extract valuable information within those documents to automate your business, reduce cost per page and manual effort.  

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Intelligent Document Processing Partners

AWS has assembled a team of partners with deep expertise in applying machine learning document processing workflows across various industries. AWS Intelligent Document Processing solutions from AWS Partners provide turnkey solutions that can help lower costs, increase revenue, and boost engagement. Go to the partners’ page to find the partner solution for your use case. 

Find your AWS IDP Partner »


Learn how to overcome document processing and analysis challenges at scale with machine learning

Watch the webinar »

Building an end-to-end intelligent document processing solution using AWS

Read the blog »

Learn more about the Document Understanding (DUS) solution

Check out the GitHub repository »

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