- Use Cases
Intelligent Document Processing
What Are Other Names For IDP?
Several names have been used to describe this category as it has emerged over the last few years. These include:
IDP automates data extraction from complex, unstructured documents that drive both back office and front office business processes. Data extracted by IDP can be consumed by business systems to drive automation and other efficiencies, such as the automated classification of documents.
Accurate automated data extraction from unstructured, complex documents is difficult, if not impossible, for legacy OCR technologies because these documents do not fit predefined templates or have too much variation.
Without IDP, enterprises have to manually classify and extract data from these documents.
With IDP, they get a fast, low-cost, scalable alternative.
Large and medium enterprises across industries and process types use IDP. It stands to provide cost savings and improve accuracy for these enterprises as they invest in managing their large volumes of unstructured data.
IDP can automate the manual data entry process at the front end of an Intelligent Automation or Hyper-Automation solution. This strategy allows enterprises to automate documents with greater complexity and variation than legacy OCR can handle. IDP helps expand the scope of your automation initiatives.
IDP performs four main functions
IDP automates data extraction from complex, unstructured documents. For most firms, this extraction is complex enough to require a trained and skilled human. IDP is powerful enough to eliminate or materially reduce the human effort required for extraction and manual data entry.
IDP can automatically classify documents into different categories based on their structure and content. More advanced IDP solutions (such as Infrrd) can accept multiple documents in a single image, then automatically split and classify them so they can be routed to the proper work queues. This automation accelerates document processing and reduces or eliminates the manual effort that can become a bottleneck for intelligent automation.
IDP validates extracted data using business rules, document comparisons, and other sources. It is important to verify extracted data to ensure accuracy. Data passing validation is sent on for processing, and data that fails validation can be corrected.
Enterprises can use IDP to analyze the data they have extracted in order to gain insights, take actions, predict the next steps, and drive better business decisions based on those insights. Be sure to ask your IDP vendor what specific functionality they offer because it can vary.
Documents are fed into the IDP process via an API.
IDP runs the documents through a four-step process.
Documents are classified and categorized in this first step, and then made ready for conversion. IDP seeks to integrate, validate, repair/impute problems, split images, organize, classify, and enhance images during this step.
The document and image are converted from an image to text (or data). Various AI and OCR engine technologies are used to the best performance. Importantly, IDP is also able to find and maintain context (such as footnotes that modify a number) during the extraction step.
The extracted data is not yet ready for consumption. The data can be enriched, extended, enhanced, validated, augmented, aggregated, classified, and understood during this step. Instead of simply extracting data from a document the way an OCR would do, IDP understands the data -- and information -- in the document. Understanding information in the source document allows IDP to extract more value (such as keeping all of the time and trend information about a graph instead of just the graph’s data points) and be more accurate than other extraction technologies.
The extracted data is now ready to be consumed. The data can be sent to the API, which integrates it into the enterprise’s IT systems, or IDP can use that extracted data to create additional value.
IDP can also use its AI capabilities to transform data into insights, automation, recommendations, and predictions. IDP can predict the next best action by identifying which document is missing from a loan application and alerting the borrower that they need to send it in. Relatedly, IDP also has Natural Language Processing (NLP) and Natural Language Generation (NLG) capabilities that allow it to write a summary report based on the extracted information, just as a human would.
IDP takes a fundamentally different approach to extraction than OCR before it. Instead of just being able to handle structured documents well, IDP can sift through more complex documents and correctly assign information, regardless of the amount of variation.
IDP can process documents with endless text and image complexity. The complex text includes text that has embedded contextual relationships (e.g., footnotes), mixed fonts, text mixed with images, long documents, and multiple document types in a single PDF. Image complexity includes noisy images, complex structures, mixed meaning, tables, graphs, handwriting, symbols, or other unusual elements.
Unstructured documents are documents where the format and location of relevant data elements change over time. That means the same data point can be found in multiple locations, depending on the document type, version, or source. OCR can’t manage these types of documents because it doesn’t know where to look if the structure varies.
While IDP excels at the most difficult extraction challenges, true IDP solutions have the versatility to handle both complex and simple documents very well, so enterprises can support a wide range of document needs with a single platform.
IDP Compared to OCR
OCR is a legacy solution focused on extracting data from simple, structured documents. OCR uses templates to constrain the extraction problem so it can increase accuracy. An OCR solution looks where its templates tell it to look on a page, and it recognizes characters. This approach is inherently tied to structured documents because it doesn’t tolerate variation well at all. When a document doesn’t fit an OCR template very well, accuracy plummets.
Many OCR vendors are trying to find a way to integrate machine learning into their solutions as a preprocessing step to stay competitive, but it's not enough to use ML as an afterthought. It's time for a new approach. That's where IDP comes in.
True Intelligent Document Processing (IDP) uses multiple AI technologies to understand a document’s structure and content. Unlike with OCR, these technologies work together to solve complex problems. For example, IDP can use computer vision to understand document structure and to identify “features” such as graphs and tables, then apply OCR to extract text from the document, and then apply NLP to make sense of the text so it can identify the data you want it to extract.
For a one-dimensional OCR solution, this complexity would be an impossible problem to solve, but IDP handles it with ease. IDP is a fundamentally different approach to data extraction that enables you to automate more documents, reduce or eliminate manual data entry, and achieve high rates of straight-through processing.
IDP uses multiple AI technologies such as machine learning (ML), natural language processing (NLP), deep learning and neural networks, computer vision, and software technologies such as OCR, UI, and workflow management. Today, a true IDP solution can use a full stack of AI technologies, working together, to automate tasks that used to require human attention and skills.
As an example, Infrrd uses AI techniques such as these in its IDP solution:
Classification (predicting two-class & multi-class categories)
Regression (predicting values)
There are various approaches to IDP integration. Infrrd’s IDP solution has two options for integration:
API access, where the enterprise systems directly access the IDP platform using APIs. One API feeds documents into IDP, and one API feeds extracted data out of IDP.
Solution access, where the enterprise logs into and control IDP (corrections, training, performance management, etc) using Infrrd’s UI. APIs are also used here to feed documents into and feed data out of the IDP platform.
IDP can be offered as a cloud or a premise solution. Infrrd offers both. A premise solution runs on the enterprise’s hardware, which is managed by its IT staff. Data is stored within the enterprise when that is a requirement. Cloud provides IDP-as-a-solution, which is secure, elastic, scalable, and has performance SLAs. We’ll help you explore the pros and cons when you get to the stage of picking a deployment model.
Infrrd’s IDP solution does not require the enterprise to have a data scientist. Infrrd manages the full data science lifecycle with its expert staff. The solution is designed to be operated by a business and process staff within the enterprise: no data scientist required. Note: Other IDP solutions may take a different approach to the use of data scientists -- be sure to check with the vendor.
Maintaining an IDP system is easy. With a template-based OCR system, humans must create and maintain document templates that tell the OCR where to find information on a page. When documents change or vary beyond the tolerances of a template, OCR accuracy suffers or fails entirely. This triggers the need to create and maintain more templates, and the cycle repeats itself. Some enterprises have several full-time employees creating and maintaining OCR templates.
IDP, on the other hand, is trained to find data in documents using machine learning, so it is far more tolerant of variation and change. There’s no need to create and maintain templates with IDP, and IDP users can train their systems to process new types of documents without building templates.