What is intelligent document processing?

Intelligent document processing (IDP) is a new, AI-driven approach to automated document classification and data extraction.

Intelligent document processing (IDP) is an artificial intelligence-powered solution used to turn unstructured and semi-structured data into a structured format for rapid and accurate information retrieval. Unlike out-of-the-box document management or processing software, IDP is tailored to the unique environment and document handling needs of a particular organization. Additionally, once the model is built and deployed, there are no ongoing software subscription costs.

We give the model, at training time, examples of the “right” answers, including examples of values for a particular form field or examples of particular document types. With lots of data and extensive fine tuning driven by the model’s mistakes, the result can be increasing accuracy through time. The alternative is to hand-code rules to make those judgements. That approach is limited by the cleverness of the engineers. Ultimately, IDP becomes better over time with more “correct” examples.

IDP Lifecycle

Understanding the IDP Lifecycle

Collect Data: The IDP model needs lots of examples of the full variety of documents that it will see when put into production.  The more and more varied raw documents you collect here, the better the final results will be.

Prepare Data: The raw documents may be PDFs. A key, but often difficult, step is to extract the content from the raw documents into a form that is amenable to the model.

Train Model: There are new machine learning models invented every day. The goal is to use the right one for associating text and annotations so that the model can, for example, recognize types and extract fields from new documents.

Evaluate Model: Sometimes you get lucky and the model works great the first time. More likely, it makes unacceptable mistakes or too many mistakes. We use mistakes to drive improvements to the model or training data or both.

Deploy Model: Lastly, we tune the model for real-time performance and connect it to existing systems. Sometimes this process exposes the need for further tuning.

…and the model repeats, learns, and improves over time, evolving as organizational and process needs evolve.

Why use intelligent document processing?

IDP replaces manual, piecemeal activities related to collating, inputting, and managing critical company data sourced from variety of documents and document types.

Intelligent document processing (IDP) doesn’t replace people, it replaces the laborious process of tagging, linking, searching, and extracting information from varieties of documents and document sources. This frees highly specialized professionals to focus efforts on more critical activities.

Some industries that particularly benefit from IDP include healthcare, legal services, retail, travel and logistics, manufacturing, and telecom. However, any company with repetitive data input and management processes can benefit. IDP streamlines workflows, improves data output, and improves companies’ revenues.

Read More

Download the solution sheet.

How Synaptiq can help

We partner with clients to devise a smart content, document, and taxonomy strategy based on user research and technology best practices.

Depending on the data and solution needed, we may implement neural networks, support vector machines, latent semantic indexing, soft set-based classifiers, natural language processing, and/or another techniques. Extraction, cracking open PDF, Microsoft Office, and other documents using natural language processing and/or machine learning, often goes hand-in-hand with the solution we deploy. This helps identify important text that can then be used in a variety of applications such as, for example, filling out forms.

Using these techniques, we create unique IDP solutions for our clients. Here are some examples:

  • Built a document classification system that was able to take millions of documents and assign them into folders. This allowed the client to move all of their documents into a document management system. Due to the massive volume of documents involved, this work could not have been done by humans and the client wouldn’t have been able to migrate to a Document Management System.
  • Built a system to extract the relevant fields from immigration documents. This saves countless hours of time spent by paralegals. (Read the case study.)
  • Built a system that could classify government requests for more information and automatically populate a response in a Microsoft Word document.

Who benefits?

Industries and companies heavily reliant on repetitive document and data input and processing.

We work with CIOs and CTO across a wide variety of sectors. However, industries heavily reliant on document processing find IDP particularly valuable as a means to improve efficiency and eliminate human-error — for example, healthcare, legal services, retail, travel and logistics, insurance, manufacturing, and telecomm.

Even clients with their own data science teams and technical resources benefit from working with us, as our team is particularly adept at mapping complex document processing lifecycles into unique machine learning models.

Relevant Reading

How BAL Uses Intelligent Automation to Deliver Exceptional Client Service

How BAL Uses Intelligent Automation to Deliver Exceptional Client Service In this case study, learn how BAL uses intelligent automation to deliver exceptional client service. Despite a march toward digitization, businesses remain overwhelmed by the need to intake and process physical documents. For many industries, such as legal services, high volumes of data impact client…

Read more
Intelligent Document Processing

A Context-Aware Approach to Entity Linking

A technical paper by Veselin Stoyanov and James Mayfield; Tan Xu and Douglas W. Oard; Dawn Lawrie; Tim Oates and Tim Finin. Entity linking refers to the task of assigning mentions in documents to their corresponding knowledge base entities. Entity linking is a central step in knowledge base population. Current entity linking systems do not explicitly model the discourse context in which the communication occurs.

Read more