In this post, we’ll cover the basics of optical character recognition (OCR) one of several AI-based technologies that will have a profound impact on the business world.
We’ll also learn how OCR and AI will impact the future of work – and why it is so important to keep up with new technology and adopting innovative tools earlier rather than later.
Optical Character Recognition (OCR) Defined
Optical character recognition (OCR) refers to the use of machines to recognize letters from an external source, such as a text document, an image, or a video. Simply by providing a text document to an OCR application, for instance, that application can “read” the individual letters in that document.
By gradually “teaching” AI what specific characters look like, the AI can learn to recognize those patterns and identify them as, for instance:
- Letters of the alphabet
- Chinese characters / kanji
In and of itself, this functionality may not be very useful.
However, when combined with other AI capabilities, such as external sensors and natural language processing (NLP), it becomes very powerful indeed, as we’ll see below.
Examples of Business Use Cases
OCR is one AI function that can augment human workers’ capabilities or, in some cases, automate business tasks completely.
Here are a few examples of how OCR can be applied in business:
- Reading and transcribing hand-written documents
- Analyzing text from one document and reformatting it into another document type
- Extracting text from images or video
Since so many jobs involve reading and analyzing text, the value of OCR should quickly become apparent.
OCR, for instance, can be used to assist with any job that involves textual analysis, data entry, transcription, or similar tasks.
Also, once that data is read, it can be immediately used as output in another application or job task.
In most cases, OCR can simply augment existing employees’ tasks – and that alone is often enough to enhance employee performance and even organizational performance. However, it may replace the need for certain types of administrative job roles, such as data entry clerks.
OCR Plus NLP, Image Recognition, and Semantic Analysis
OCR becomes especially powerful when it is combined with other AI functions, such as NLP, image recognition, and semantic analysis.
Here is a quick breakdown of these other types of functions:
- Image recognition learns to recognize objects within pictures
- Semantic analysis and natural language processing (NLP) can analyze the semantic meaning of text
- Sentiment analysis can analyze and categorize the emotions of a piece of text
Here are a few examples showing how these capabilities can be combined in a business setting:
- Reading and summarizing legal documents. Reading and analyzing legal documentation is a time-consuming task within the legal field. AI is already being used to automate many tasks in this field that require reading, textual analysis, and semantic analysis, saving both time and money.
- Reading and compiling receipt data. Google has introduced a feature in its suite of applications that allows users to scan their receipts and automatically incorporate that data into their personal budgets. Rather than needing to manually write down expenditures, all a user needs to do is take a picture of the receipt and Google’s OCR-based app will input, analyze, and categorize that information for them.
- Transcription apps. There are many, many apps, both on the web and in app stores, that automatically recognize and transcribe text from images. Use cases for these apps can include everything from transcribing recipes to storing product information to translation.
- Product recognition. Some consumer-oriented apps use a combination of image recognition and text recognition to recognize products on store shelves. Amazon has an app, for example, that can analyze images of products, pull that product up in the app, then allow the customer to compare prices on Amazon.com.
In short, OCR can be implemented anywhere there is a need to read text – and since that is such a universal need, its applications are equally universal. As we have seen from the examples above, for instance, OCR can be used for both consumer-oriented apps as well as business apps.
When assessing how valuable OCR can be in one’s work environment, it is useful to begin by breaking down the job by task and focusing on those that require text recognition – that is, reading.
Every job is unique, after all, and will have a unique set of tasks.
An administrative assistant, for instance, may spend about 5% of their time performing tasks that require reading. So an OCR app will certainly not replace their job completely – rather, it will take over just those OCR-related tasks, which can free up human time for other activities.
When it comes to the impact of automation and AI on the workplace, it is important to understand a few points.
Namely, it is important to recognize that OCR just recognizes letters.
Alone, OCR can improve the efficiency of jobs or take over certain job tasks. Or, as we have seen, OCR can become the basis for certain types of apps.
However, AI can segment and automate other tasks, such as image recognition, pattern recognition, semantic analysis, and sentiment analysis. Each of these functions, in turn, can then become recombined into apps that are quite powerful.Taken together, these separate AI capabilities can significantly accelerate employee productivity and automate an increasing number of job tasks.