The intelligent automation industry is ever evolving, and one outcome of this is the coexistence of technologies that, on the surface, appear to be very similar to each other. Maybe one’s even the more evolved version of the other. As a result, they get easily confused.

This couldn’t be truer for optical character recognition (OCR) and intelligent document processing (IDP). They’re similar but different. Let’s examine how both technologies work, investigate use cases where the two shine, and ultimately answer the question, “Which technology is a better fit for my situation?“

OCR vs. IDP: Which Will Better Solve Your Challenge?

OCR vs. IDP: The Difference

What is OCR?

OCR scans documents, PDFs, or photos, optimizes their quality, and uses pattern recognition to extract printed and handwritten text from them. Then, it converts the extracted text into an electronic form that’s searchable, editable, and can be read by machines, and corrects any errors before uploading the content into a document management system.

Pros:

• It’s incredibly accurate when handling structured text in many languages, especially when the format is well defined.
• It accelerates document processing, especially if you primarily process documents with structured data, which increases overall output.
• It comes with significantly fewer errors than manual data entry.
• It’s relatively inexpensive to implement

Cons:

• It’s limited to structured data, like online forms and time sheets.
• It can be inaccurate when tasked with complex images, certain fonts and layouts, handwriting that’s difficult to read, and unclear scans.

What is IDP?

You can think of IDP as the “next generation” of OCR in a way, as it does incorporate what’s known as deep OCR technology, which uses intelligent technologies like deep learning, neural networks, machine learning, natural language processing (NLP), and AI.

The process goes like this: The machine learning algorithms and AI extract the data on the document, check for its accuracy using logic-based algorithms, and comprehend the data in context, allowing IDP to handle structured, semi-structured, and unstructured documents. Then, IDP recognizes the document’s format, classifies it accordingly, and sends it to the appropriate location. It can even initiate workflows for certain document types.

Pros:

• It can recognize text in almost any font, size, or layout, even if there are checkboxes, low-quality scans, or a distortion in the text, like faded or broken letters.
• If needed, it can automatically divide files and document batches into individual documents without requiring barcodes or separator sheets.
• It offers more expert decision-making skills to your content’s processing than OCR can alone, allowing it to overcome many of OCR’s limitations.
• It accelerates document processing regardless of document format or type, which increases overall output.
• It reduces the error rate even further than traditional OCR.

Cons:

• It’s generally more expensive than traditional OCR.
• If you typically process documents with structured data and have straightforward workflows, you may not have the opportunity to make the most of all IDP has to offer.

OCR vs. IDP Use Cases: Find the Right Fit for Your Challenge

OCR Use Cases

Converting Text to Speech

Since OCR can handle text in many languages, it can turn text into spoken words in a variety of contexts, which can help make information more accessible to people with visual impairments.

Reducing Fraud and Enhancing Security for Banks

OCR effectively scans transactions, loan agreements, and checks within banks by checking documents for authenticity. This reduces banks’ risk of fraud and improves security.

Digitization in Healthcare

OCR can digitize medical records and handwritten prescriptions, easing the process of verifying and filling prescriptions.

Now, Consider Your Use Case

In other words, OCR might be the best choice for you, despite its limitations, if your challenge:

• Involves repetitive, simple key data points.
• Uses simple tables containing a limited number of line items.
• Deals with straightforward workflows.
• Handles standardized documents like invoices and forms.
• Cost efficiency is a top goal for you.

IDP Use Cases

Customer Onboarding

IDP can accelerate the processing of documents like contracts, customer intake forms, and NDAs, processing them with high accuracy levels and promptly directing validated documents to the appropriate downstream systems.

Insurance Claims

IDP speeds up the approval or rejection of claims by eliminating manual data entry and reducing error rates. It can even cross-check data extracted from relevant documents against existing policies to authenticate claim validity, ensuring that only valid claims are processed.

Invoice Processing

IDP can efficiently process multi-page documents, PDFs with multiple invoices, skewed invoices, tables with a high volume of line items, and multiple tables crossing numerous pages, all of which are common issues for AP departments.

Legal Contract Management

IDP lets legal professionals analyze contracts and confirm any legal obligations quickly and easily. It can also accelerate contract creation by extracting key terms and helping to incorporate them into the new agreements.

Virtual Mailrooms

IDP is often used to transition paper-based mailrooms into efficient virtual ones. Incoming paper is digitized, and emailed documents are converted so they’re ready for processing. Then, these digitized documents are routed based on your specified business rules using workflows, where they’re securely stored in the proper location and easily located by staff.

Now, Consider Your Use Case

The bottom line is that IDP might be your ideal match if your challenge:

• Involves processing high volumes of documents.
• Includes workflows with unstructured or semi-structured data in documents.
• Requires intelligent document classification and extraction.
• Calls for automation with more advanced capabilities than text recognition, such as intelligent document classification, extraction, and validation, and AI-driven analytics.
• If scalability or integrating with other systems is a top goal for you.

OCR vs. IDP: Get More Guidance

As we’ve seen, both tools are incredibly useful, but they have different strengths and weaknesses. If you want more guidance on how to select the right tool for you, drop a comment in the chat below, we’d be happy to help.