Reading emails, scanning contracts, manually processing invoices—the tedious tasks related to document processing can jam up your business operations. Document processing is a prime candidate for automation, but the technology is advancing so fast, it can be hard to know where to start or when it is time to modernize.
OCR (Optical Character Recognition) and IDP (Intelligent Document Processing) are two approaches to tackling business documents. To determine when to choose IDP over OCR, we’ll explore their definitions, their differences, and the places these approaches dovetail as document processing technology evolves. We will also provide a side-by-side guide to compare the two at a glance.
The original: OCR
OCR is a foundational technology that scans documents and converts the text from an image into searchable, machine-readable text.
OCR has been around a very long time. The term optical character recognition was first introduced by IBM back in 1959, and some of the principles behind optical scanning were developed as far back as the 1870s. With roots in adaptive technology to help the visually impaired, OCR relies on pattern matching. It “looks” at a document in a way that is similar to how the human eye would, and identifies the characters on the page. It turns an image of "ABC" into the text string "ABC,” essentially transcribing what it sees.
OCR limitations
Well-established and commoditized, OCR offers cost-effective automated document handling, but only in specific circumstances. It is great for simple digitization tasks such as converting paper archives into searchable records, or digitizing old books or simple forms with a fixed layout. However, OCR systems are very limited in what they can and cannot process and in how you can use the data afterward. For example:
Low-resolution scans, poor image quality, and handwritten text make it impossible for OCR to recognize the characters.
OCR tools need to be trained on each document template to succeed. When OCR encounters a new template—for example, an invoice from a new vendor—the system will fail without new training.
Complex layouts, such as tables or mixed fonts, cause confusion.
OCR can't handle these hurdles because it lacks contextual understanding. It “sees” the text, but it has no capacity for evaluating meaning. It can read “1225” but can’t identify if that’s a price, a date, an invoice number, or part of an address. Other systems, or more likely human employees, are left to interpret the text.
These limitations can significantly impede business progress. Consider the experience of one large insurance organization that used an OCR-based solution. They used various approaches to try to make use of the resulting digitized text, including the use of structured templates, but those efforts were difficult to maintain. Because the system couldn’t manage document variability, humans often had to intervene. Some documents were still taking up to 6 hours each to review. This frustration was what prompted them to move to a more modern IDP system.
The OCR/IDP overlap
While IDP represents a significant step forward in document handling, OCR remains not only a good choice for some organizations but also a complementary technology to IDP in certain use cases. For example, OCR registers exactly where in a document each piece of data is extracted from. If you need to reconcile or validate information later in a workflow, your OCR data is able to point you to precisely the right spot. Modern IDP tools incorporate OCR functionality or integrate with an existing OCR system.
Choosing IDP vs. OCR
IDP is an advanced, AI-powered technology that is tremendously useful for large enterprises with many types of documentation. However, there are still uses for OCR, and ways that the two technologies work together. If you are still not sure which is which, here’s a side-by-side comparison of the two technologies.
OCR | IDP | |
What does it do? | Text recognition and extraction | Contextual understanding, classification, and validation |
How does it work? | Image processing, pattern recognition | OCR + AI, machine learning and natural language processing (NLP) |
What does it handle best? | Highly structured documents in fixed templates | All document types, structured and unstructured |
How flexible is it? | Low flexibility. Struggles with document layout changes | High flexibility. Uses context to learn and adapt |
What do you get? | Plain text, searchable PDFs | Structured data ready for business systems |
How much does it automate? | Data capture | End-to-end workflows |
The winner of IDP vs. OCR
The need to digitize and automate document handling isn’t going away. OCR is a solid but limited option, while IDP is a powerful choice that leverages new AI functionality. In deciding when to choose IDP over OCR, consider the problems you need to solve. Organizations with complex processes, multiple document types, or changing document formats can benefit from the flexibility and contextual understanding of IDP. Modern IDP solutions incorporate OCR and integrate with other business systems, making it an invaluable part of your business processes.
Interested in exploring IDP? Appian DocCenter is a high-accuracy IDP solution powered by generative AI. Learn how it takes document automation to the next level.
