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Intelligent Document Processing

What is Intelligent Document Processing?

Intelligent Document Processing (IDP) is an advanced AI technology for automated batch processing and extracting data from large volumes of documents. IDP applies sophisticated computer vision algorithms and artificial intelligence to automatically detect and capture required data from different types of documents. The commonly processed documents include invoices and purchase orders, contracts, receipts, multiple forms, surveys, ID documents, insurance claims, and more. Intelligent document processing saved years of manual work for banks, financial services, insurance, healthcare, government, logistics, and other industries. 


IDP eliminates the need for manual document sorting and significantly reduces the number of errors associated with manual data entry. While no IDP system can guarantee 100% accuracy of data entry, it provides tremendous value by automatically capturing 80-95% of data, cross-checking data with other sources, and highlighting the areas requiring human validation. Now instead of manually typing all the data fields, human operators only need to verify about 10-20% of the data and correct a few possible errors. IDP makes document processing faster, more accurate, and cost-efficient – leading to higher productivity and fewer mistakes in critical business processes.




What does IDP technology do?

IDP technology enables end-to-end processing of large document batches collected from various sources, including digital PDFs, scanned documents, mobile photos, faxes, and other formats. They may come in packages, combining different documents, like mortgages. In this case, all documents must be separated, identified, and processed individually. Then the data from these documents could be cross-checked to ensure its accuracy. 

Here are the typical processing steps performed by an IDP solution

  • Document input 
  • Document classification and separation. 
  • Data extraction
  • Data verification
  • Data and export

Document input

Documents may come into an organization from various sources and in different formats. This step aims to collect all the documents to be processed efficiently. 


Most innovative organizations receive documents via email as an attachment or download from web portals (e.g., via DocuSign). In this case, documents are usually high-quality, digitally generated, and comparatively easy to process. 


However, many traditional enterprises and government agencies still receive paper documents via mail. These documents require scanning, which may introduce additional issues, like skewed pages, low-quality scans, and overlapping colors, significantly increasing processing complexity. 

It can get even more complicated. Healthcare organizations still widely use faxes, which are very low-quality images, and extremely hard to process. Some companies collect mobile photos of documents, including checks, ID documents, receipts, credit and insurance cards, tickets, and other small-size documents. Those are also hard to process since people often take dark, blurry, shady pictures on complex, hard-to-remove backgrounds. 


Intelligent Document Processing solutions can process all these documents. However, they often use special connectors or RPA bots to collect documents from various sources, like email attachments, scanning hot folders, FTP servers, and other locations.



Document classification and separation

Multiple documents coming from scanners or faxes are often combined into large packages. Think about 200-300 pages long mortgage applications that you sign. For processing, all these documents need to be separated and identified. Similarly, an email with an invoice may also contain other attachments, so the system should be able to sort documents automatically and select only those that need to be processed.


Document classification helps check the completeness of the package and identify missing documents before extracting all the data. 


From the technology perspective, document classification is the initial step that allows choosing and applying the suitable ML model for data extraction. 



Data extraction

The purpose of data extraction is to read and transform unstructured data from the documents into a structured form that could be used for automatic processing.


Data extraction from documents typically starts with using optical character recognition (OCR) technology to recognize and extract text from scanned images or even some electronic documents. The extracted text is then processed using natural language processing (NLP) technology or machine learning algorithms to identify and capture specific data fields, such as names, addresses, and dates.


The extracted data is organized into a structured format that can be easily accessed and used by other applications or bots for automated processing.

Data extraction from documents is a complex process that never guarantees 100% accuracy of the extracted data. The accuracy depends on the quality of the original document, the capabilities of the OCR and NLP algorithms, and the complexity of the data elements being extracted.



Data verification

Because extracted data is never 100% accurate, it always requires verification.


Data verification is essential in intelligent document processing, as it helps ensure the reliability of extracted data. There are two ways to check data accuracy: 


  • Automatic verification
  • Human verification

Automatic data verification typically involves comparing the extracted data against a set of pre-defined rules or cross-checking existing databases and other trusted data sources to determine whether data is correct and complete.


For example, a data verification rule might specify that a date must be in a specific format or that a name must consist of a certain number of words. If the extracted data does not meet these criteria, it might be automatically corrected or flagged for human review. 


For human verification IDP solutions usually provide split screen graphical user interface, that shows on one side the location of the source data in the original document and on the other – the extracted data fields. ML models used for data extraction can usually calculate the confidence level of data recognition and highlight low confident data, what requires human review. This saves time for human operators while they focus on uncertain data fields. 



Data export

Once the data has been validated and deemed to be accurate and complete, it needs to be exported to the back-end applications that use it. Again, there are different ways how data can be exported, depending on the specific requirements and capabilities of the back-end applications. 


For example, the data could be exported as a CSV or Excel file, which can be easily imported into many different types of applications. Alternatively, the data could be exported using a specific data interchange format, such as XML or JSON, which is designed for transferring data between applications.


In some cases, the validated data may be sent directly to the back-end application using an application programming interface (API), which allows the two systems to communicate with each other in real time.




What is the difference between IDP and OCR?

OCR, or optical character recognition, is a technology that is used to recognize characters and read text from scanned images. It uses pattern recognition algorithms to identify the individual characters in an image and convert them into machine-readable text. A combination of characters forms words that are verified by embedded dictionaries. OCR is a critical component of intelligent document processing (IDP), providing the foundation for extracting data from documents. 


Intelligent document processing (IDP) takes it to the next level. It goes beyond OCR and leverages other technologies, such as natural language processing (NLP) and machine learning, to identify the exact data that needs to be extracted. These could be structured or semi-structured data fields on forms or invoices. Or these could be specific clauses in contracts and legal documents described in unstructured text.


IDP also provides additional capabilities, such as connecting 3rd party data sources for automatic data verification, UI for human verification, and connectors for document import and data export. Some advanced IDP solutions offer capabilities for custom training of ML models for specific document types or domains. Some of them can leverage the results of human verification to enhance the ML model.  


In summary, OCR is the basic technology of reading text from images, while IDP transforms unstructured content from documents into structured data that could be used in automated processes. 




How does IDP work for businesses?

Data is a key asset, and every organization deals with two types of data: 


  • structured data that could be put in a spreadsheet or a database table 
  • unstructured data that includes documents, images, videos, voice, etc. 

80-90% of the data is unstructured, meaning it’s not easily accessible and cannot be used for automatic processing unless it’s converted into a structured form. 


Documents contain the most critical business data in unstructured form. It is essential for the success of any organization to be able to process documents efficiently. 


This is why IDP solutions are so valuable for every business. 




The benefits of Intelligent Document Processing

IDP offers numerous benefits to every sizable organization: 


  • Saving up to 90% of time and resources on manual document processing
  • Reducing the number of errors as human operators are focused on data verification rather than typing. 
  • Achieving higher customer satisfaction by providing faster and higher quality service, including order processing, invoice payments, insurance claims processing, and accelerating other document-related processes. 



How does IDP work with RPA?

Intelligent Document Processing (IDP) and Robotic Process Automation (RPA) often work hand-in-hand, automating document-specific processes. While IDP extracts data from documents, RPA enables document input from various sources and data export to downstream applications. 


Unique flexibility of RPA technology allows it to connect to virtually any document source: collect email attachments, check scanner hot folders, access fax servers, FTP, and web repositories. 


RPA bots can also pick up exported data in any format and upload it to the back-end system via API or user interface. 


Together RPA and IDP technologies enable end-to-end document processing.




What are the use cases for IDP?

Intelligent document processing use cases are usually associated with processing specific document types. There are common horizontal and industry-specific vertical use cases.


Horizontal use cases include


  • Transaction documents: invoices and purchase orders
  • Legal documents: agreements and contracts
  • HR documents: resumes, IDs, and various forms
  • Tax and expense documents and receipts.

Vertical use cases differ for industries, yet the most common are:


  • Mortgage and loan processing in banking 
  • Claims processing in insurance 
  • Forms, census surveys, voting ballots in government
  • Shipping documents in transportation, and others. 

Depending on the document type and the source, an IDP solution may use different imaging technology and ML models to support specific use cases. 


How to choose IDP software?

Intelligent document processing technology can be packaged in various formats. 


  • Pretrained ML models for specific document types available as a cloud service via API (e.g. Google and Microsoft Document AI) or basic OCR services (e.g., Amazon Textract). These services are designed for developers who can package them into an application or an RPA bot, so business users can work with them. 
  • Business user-friendly platforms that provide convenient UI for uploading documents, verifying data extraction results. Some advanced platforms also allow the selection of different AI models or the training of a custom model for specific documents. 

Some vendors provide packaged solutions with pretrained models, while others offer highly customizable platforms requiring additional services to tune models for particular use cases. 


Usually, vendors have more experience working with specific use cases or industries; therefore, requesting references and success stories from your area of interest is essential. 


You may review a list of IDP vendors and their expertise in the RPA Master catalog




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