It is sometimes difficult to keep track of the flood of new terms on the Internet. This is not much different in the case of IDP (Intelligent Document Processing) and OCR (Optical Character Recognition). If you don’t take a closer look at these terms, you can easily see IDP as a simple rehash of OCR. But this is not true. An overview of the differences between IDP and OCR.

OCR is the bumpy path next to steeply sloping terrain, while IDP is the six-lane highway along the coast

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What OCR is and what OCR can (and can’t) do

OCR is nothing new to document capture but has a few advanced years under its belt. Traditional OCR, simply put, converts an image of text into machine-readable text. This process can be valuable for simple document digitization but leaves a lot to be desired beyond that. The two main problems with traditional OCR are:

a) Traditional OCR is based on a template, i.e. documents to be processed must be formatted according to certain rules, otherwise, OCR cannot do anything with the documents.

b) Conventional OCR cannot extract any context from the content, i.e., interpretation of the data and thus automated end-to-end processes are not possible.

OCR may be the solution in a few cases, such as when there is only one rule-based form. However, the reality in terms of the variation of documents that an organization works with on a daily basis is significantly different. Some fundamental issues for businesses cannot be solved with traditional OCR:

  • As soon as semi-structured, unstructured, and handwritten documents have to be processed, traditional OCR is no longer suitable; the creation and maintenance of templates for all formats is far too time-consuming and expensive
  • This makes traditional OCR unsuitable for large-scale implementation and scaling

IDP is a combination of different intelligent technologies

What IDP is

As mentioned at the beginning, IDP is not a cheap rehash of OCR. To be fair, however, the advances OCR has made over the years have made IDP possible. Or to put it another way, while OCR has nothing on IDP, IDP continues to use OCR as part of a powerful combination. OCR converts an image of text into readable text, and intelligent and advanced AI technologies, including machine learning and deep learning, do the rest. On the one hand, this makes IDP capable of mimicking cognitive abilities, i.e., capturing documents correctly, classifying them correctly, and extracting all relevant data from them. In addition, this relevant data can then be automatically fed into the correct workflows for further processing.

IDP is able to close the large gaps that traditional OCR has left in automated document capture.

What IDP can do

IDP, as a combination of advanced AI technologies that combine powerful OCR, machine learning, and deep learning technologies, can process an enormous variation of documents with great variability. We’re talking about semi-structured, unstructured, and complex documents, often with handwritten text, as they are received and processed daily by companies across all industries. IDP not only recognizes and captures the content but above all extracts the context from this content. The example of a traditional bank check (which may well be in circulation depending on the country) with wide variation is a good illustration of the power of IDP’s AI technologies:

  • Unstructured form: Depending on the bank, the check looks very different
  • Digital text: Text printed by the bank
  • Handwritten text: Text handwritten by the issuer, e.g., amount and to whom issued
  • Amount in numbers: $1000
  • Amount in text: One thousand dollars

IDP not only learns continuously with Machine Learning and Deep Learning models, but IDP also interprets context from content thanks to advanced AI technologies, so it knows that ‘$1000’ and ‘One Thousand Dollars’ are the same thing. IDP simply does similar things that previously could only be done by humans better and, most importantly, much more effectively. IDP mills through data like no human ever could nor will be able to, collecting, analyzing, and outputting relevant and structured data from unstructured documents.

IDP brings context to content

The benefits of IDP for businesses

IDP brings massive benefits to businesses with its combination of intelligent and advanced AI technologies:

  • IDP enables end-to-end processes from capture to document extraction to the right workflow or the appropriate employee
  • Employees are freed from manual and tedious tasks such as typing and capturing data and can turn to tasks with greater value and direct relevance to their skill sets
  • Minimization of errors through automation
  • Cost reductions while increasing process speeds
  • High flexibility for scaling
  • In the event of specific problems, the human being comes into the loop and can intervene in a targeted manner