What Is Intelligent Document Processing?
Intelligent Document Processing (IDP) is a method of automating the collection of structured, semi-structured, and unstructured data from a variety of sources and organizing it into a usable format. IDP is the most advanced form of extracting data from documents.
Keep in mind that IDP is not the same as Optical Character Recognition (OCR), despite the fact that the two terms are often used interchangeably. Instead, IDP was developed to enhance the capabilities of OCR, as well as to incorporate other technologies, such as data capture and Natural Language Processing (NLP).
With this in mind, this blog post covers how IDP works. But before we get into that, let’s talk about the benefits of IDP.
IDP benefits
With the ability to eliminate the need to collect unstructured and semi-structured data manually, IDP delivers a number of critical benefits for a wide range of organizations. These include:
- Increased processing speed – AI-native IDP solutions can increase the speed at which data is extracted by as much as 10 times.
- Increased accuracy – Accuracy for data extraction is as high as 99.9%, and straight-through processing of over 95% can be achieved.
- Enhanced productivity – With minimal human intervention required, employees aren’t tied to the manual processing of data. This means more data can be processed more quickly, while employees focus on higher-value tasks.
- Costs less – With the increased speed and accuracy of document processing and the elimination of manual data entry, cost savings are as high as 70%.
- Electronic storage of documents – Paperless processing of documents allows for the digital storage of those documents.
- Integration at the business level – IDP can easily be integrated with existing business systems and other automation solutions, allowing organizations to achieve a fully integrated robotic process automation (RPA) system.
These benefits are significant, particularly in a world where organizations increasingly have to do more with less. With this in mind, let’s take a look at the IDP workflow and how it helps organizations reap these benefits.
IDP workflow
The IDP workflow is used to scan hard-copy documents and files, capture the information in them, and store that information in a digital format. Types of documents that can be scanned include PDF files, emails, text messages, medical imaging, forms, and other types of documents in digital and paper-based formats. With this in mind, the IDP workflow consists of five steps, which are as follows:
1. Preprocessing of the document
Documents must be preprocessed to ensure that OCR can effectively distinguish the characters and words from the background. For this reason, the following techniques are used to prepare the document for OR:
- Binarization – This is the conversion of a color image into black and white. It is done at the pixel level, converting each color pixel into either a black pixel (value = 0) or a white pixel (value = 256), so there is an easy distinction between the characters or words and the background.
- Deskewing – Horizontal misalignment of a scanned document is common. There are a few techniques, such as the Topline method and the Projection profile method, that are used to correct the skew of the scanned image.
- Removal of noise – This is the process of removing small patches or dots of contrast that are not part of the data and might be picked up by the OCR solution.
2. Classification of the document
Classification of the document is a three-part process that determines the following:
- Document format – This is the identification of file type, such as JPEG, PDF, TIFF, or MS Word.
- Document structure – This determines whether the document is structured, semi-structured, or unstructured. Structured documents, such as an application form, have a consistent template into which information is entered. Semi-structured documents, such as invoices, have similar information that can be entered into different areas of the document. Unstructured documents, such as an email or contract, have little to no structure or formatting to them.
- Document type – This determines the type of document, such as whether it is an invoice, shipping label, email, or bank statement.
3. Extraction of data
There are two ways to extract data from a document. These include:
- Key-value pair extraction – This is a method that extracts specific values that represent unique identifiers in the document.
- Table extraction – This involves the extraction of line items arranged in a table format.
- OCR – This is the conversion of documents with typed or handwritten text, such as scanned documents, PDF files, or photographic images, into text that has been machine-encoded. Keep in mind that there are errors that can happen with OCR, including errors in word detection, word or character segmentation, and character recognition.
- Rules-based extraction – This is ideal for the extraction of data from structured and semi-structured documents because it uses a position reference within the document to identify key-value line items or pairs.
- Learning-based extraction – This uses machine learning (ML) and deep learning (DL) to extract data. Supervised (human involvement) and unsupervised (no human involvement) learning is used to train the extraction models and increase their efficiency and accuracy.
- Document type – This determines the type of document, such as whether it is an invoice, shipping label, email, or bank statement.
4. Validation of data
The validation of extracted data is done to determine whether it contains any inaccuracies. This is done by applying data validation rules to the document, ensuring that any inaccuracies that are present are detected and flagged so they can be corrected.
5. Review by a human
The IDP workflow would not be complete without the human component. All flagged documents are reviewed by a human to confirm and correct inaccuracies. This is particularly useful during the supervised learning of learning-based extraction.
Once the IDP workflow has been completed, the resulting data can then be entered into a database or exported to any one of a number of file formats, such as PDF or XML.
IDP Capabilities
There are numerous capabilities that come with an IDP solution. These include:
- Identifying content that is difficult for traditional OCR to read from a document
- Determining how relevant a word or character is based on pre-defined rules and the context of the document
- Extracting specific information
- Auto-classifying documents
- Validating data
- Reading QR codes and barcodes
- Organizing data
- Generating reports
IDP use cases
The above capabilities are applicable to a wide range of industries. The following are specific use cases for IDP:
- Lending and insurance – IDP can be used to process loan applications and conduct a credit risk analysis.
- Commercial real estate – IDP can be used to process a variety of documents, such as lease agreements, rent roll processing, and T12 statements.
- Accounts payable – IDP makes it possible to process invoices with a variety of layouts and structures, ensuring that clients have a seamless experience while being able to match the invoices against purchase orders in real-time.
- Logistics – IDP allows the easy and seamless management of data from one end of the supply chain to the other and makes it possible for companies to deliver necessary documents to vendors, contractors, and transportation teams.
- Healthcare – IDP is invaluable when it comes to processing documents related to patient intake and onboarding, as well as revenue cycle management.
Embrace IDP and Get IT Working for You
The key to taking full advantage of IDP is to take a strategic approach to implementation. This includes implementing process intelligence, which is used to examine your processes and determine where IDP implementation will be most effective, as well as identifying process inefficiencies that could interfere with the implementation of IDP.
At Tangentia, we have an experienced team who can work with you to determine your IDP needs and develop a solution that will enhance your document processing capabilities.
For more information, reach out to a Tangentia ream member today.
George Bennett, VP of Business Development at Tangentia says “I am honored to be part of an organization that is rapidly expanding and delivering impactful solutions to its customers that change the way people work. The need and demand for Robotic Process Automation will continue to grow as more organizations of all sizes look to ensure business continuity and streamline their workforce.” George is a graduate of the prestigious West Point Military Academy and has managed more than 300 million dollars in revenue when at FileNet which was bought by IBM. He was also a member of the US WaterPolo Olympic team.
