If you follow technology and business news, you have likely heard the term “optical character recognition” (better known as OCR) before.
When you hear about OCR, it is frequently in conjunction with other technology-related terms such as machine learning (ML), deep learning (DL), and artificial intelligence (AI). How does OCR vary from machine learning (ML), deep learning (DL), or artificial intelligence (AI)? And what is its practical application today?
Here’s a brief primer designed to give you a working knowledge of OCR for modern business.
What is OCR and how is it used in document digitization?
Briefly, optical character recognition is "a technology used in the process of taking an image like a scanned document or photo and attempting to extract text from it,” explains David Young, senior staff engineering ML tech lead at Ripcord.
OCR is a key tool when a business wishes to digitize, classify, and ultimately extract important data from mountains of documentation. It is responsible for determining -- on its own and without human intervention -- the difference between two similar-looking words or sentences, or between two letters and numbers. For example, it is OCR which uses context to help separate zeros from the letter O in documentation, Young notes.
Are OCR, AI, ML and DL really just all synonyms?
These terms are linked, but they are not interchangeable. OCR is typically a task of DL, which is itself a subset of ML that allows systems to 'learn' automatically and improve over time.
Can OCR do more than simply recognize characters?
As mentioned above, OCR is generally an ML task, and ML has become crucial in "incorporating context in what a detected object means," Young said.
In the case of an unstructured document that includes the hand-written sentence "I threw the ball to my dog," for example, OCR might be called upon to determine that "dog" is, in fact, "dog" and not "cat" or some other three-letter word. The process uses context, which is in this case mention of a ball, to figure out the writer's meaning.
After all, "I wouldn't throw a ball to my cat," Young said. "The higher-level contextual clues are where ML and DL are becoming king."
How Ripcord uses OCR for its clients
Used in conjunction with related technologies, OCR can provide even more valuable, nuanced insights to companies that choose to make use of it. Ripcord's ML algorithms use DL on a vast, constantly growing dataset to enable high-accuracy OCR. Ripcord clients can be confident that, when our solutions digitize and extract data points and other entities from documentation, we've gotten both the content and context correct. This not only saves untold numbers of personnel hours, but also yields now-'unlocked' information of which the customer was previously unaware, owing either to inability to recognize unstructured text or the sheer volume of documentation.
Thanks in large part to OCR, once a customer's content is digitized with Ripcord, all of its data is now structured, searchable, and actionable.