Ocr meaning

OCR: Meaning and Top Business Applications

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Optical character recognition (OCR) is a new AI-driven technology that can be used to automate a number of business tasks. In this post, we’ll outline an OCR meaning, plus examples of OCR, its benefits, and related AI technologies.

OCR: Meaning and Business Applications

Optical character recognition (OCR) refers to the recognition of letters within images.


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It works this way:

  • An OCR app is trained to recognize patterns – specifically, words and text – from a certain alphabet, such as English
  • Users provide the OCR software with an image
  • The OCR application extracts the individual letters from that image
  • Those letters are then passed to another application for further processing

From one perspective, we can think of OCR as a micro task – that is, it simply “reads” text from an image. 

In and of itself, this may not be considered a job task. Yet when combined with other AI-driven linguistic functions, OCR becomes very powerful.

Examples of How OCR Is Used in Business

Here is a list of several ways that OCR is being used in today’s business world:

  • The digitization of physical business records
  • Digitizing customer forms and documents
  • Reading receipts and automatically entering that information into accounting software
  • Extracting text from an image in one language and translating that text into another language
  • Extracting text from an image and reading it out loud
  • Digitizing old articles from magazines or newspapers
  • Extracting text from an image and analyzing the meaning of that text for, for example, marketing purposes
  • Extracting text from an image and using sentiment analysis to understand the emotional content of that text
  • Digitizing books

Ultimately, the workflow in question will dictate how useful OCR will be.

Some organizations can benefit greatly from OCR, such as those that need to continually transfer data from physical documents to digital ones. 

Financial institutions are one example – since they deal extensively with physical documents such as receipts, invoices, and forms, OCR can save a great deal of time and money.

Should Your Organization Use OCR Technology?

There are several advantages to using AI technologies such as OCR.

A few of the biggest include:

  • Increasing employee productivity
  • Cutting costs
  • Saving time
  • Increasing organizational agility and efficiency
  • Creating new products, features, and services
  • Improving the customer experience
  • Enhancing the employee experience

Naturally, not every organization can derive all of these benefits. The advantages actually gained will, of course, depend on the organization’s needs. 

When assessing the potential value of OCR technology, here are a few steps to follow:

  • Assess jobs and, more specifically, job tasks and workflows that involve reading physical text documents or images
  • Identify the most time-consuming and costly tasks
  • Weigh the benefits and costs of adopting an OCR tool
  • Research OCR software solutions that address those problems
  • Create a structured digital adoption strategy for implementing that tool within the organization
  • Manage the change project carefully and make adjustments as needed
  • Reinforce the change over time to ensure that it sticks

The more that an organization spends on tasks that require textual analysis and data entry, the bigger the advantages of implementing OCR tools.

Digitization, as mentioned, is one of the most common use cases of OCR technology. And since more and more companies are moving to electronic records, this type of technology is quickly becoming standard.

However, when OCR is used with other AI-driven technologies, its possible use cases increase exponentially.

Beyond OCR

Natural language processing (NLP) refers to a set of AI-driven technologies that revolve around language.

OCR is one example, but there are others.

For instance:

  • Sentiment analysis – Analyzing the emotions within a text
  • Semantic analysis – Analyzing the meaning of a text
  • Text summarization – Providing a concise summary of a text
  • Machine translation – Translating text from one language to another
  • Text generation – Generating new text from more concise inputs

All of these are examples of sub-categories, or techniques, within NLP. They can be used in conjunction with one another to automate even more business functions, above and beyond text extraction and digitization.

Here are a few examples:

  • Chatbots are AI-powered apps that can interact with users to automate and improve certain stages in the customer journey, such as customer care and technical support
  • Semantic search has become common in search engines and attempts to discern users’ intention when they search, rather than basing results strictly on keywords
  • Text generation is still in its early stages, but it can be used to automate certain types of writing tasks, such as basic reporting or copywriting
  • Machine translation can be used to automatically translate text, including everything from product reviews to social media comments to product documentation to articles
  • Sentiment analysis can be used in brand monitoring tools to offer insight into customer sentiment, behavior, and needs

As these types of technologies become more sophisticated, we can expect to see even more AI-driven automation, which will fuel performance improvement, business agility, innovation, and more.

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