OCR technologies refers to technologies that use “optical character recognition” – the extraction of written characters from images – and AI techniques to automate certain tasks, such as the digitization of business records.
Such automation technologies will have a profound impact certain business functions, jobs, and even the way businesses operate.
As we will see in this post, OCR technologies, especially when they are used with NLP technologies, will save businesses time, money, and more, generating efficiency and performance improvements that can generate significant bottom-line impacts.
What Is OCR Technology?
OCR technologies, as noted, are designed to “read” written characters.
- “Learn” what characters constitute a written language through repeated training
- Look for those characters within an image, such as a digital photograph
- Turn that information into text that can be read by machines or humans
This readable language then be further manipulated by other technologies, such as NLP technologies, which we will discuss below.
Here are a few of the most common use cases of OCR:
- Business record digitization. Digitizing business records involves transferring hard copy records to a digital format and has become standard for organizations that are in the midst of workplace digitization. Examples of digital business records include digital invoices, electronic healthcare records, and digitizing any other form of paperwork used in a business.
- Digitizing IDs. Digitizing IDs can be useful for organizations such as government agencies, security offices, HR departments, and customs and immigration officials. Optical character recognition, used in conjunction with scanning devices, can scan and automatically extract information from these IDs and store it digitally. This not only saves labor costs and improves efficiency, it also increases security, since IDs no longer need to be stored physically.
- Digitizing books. The digitization of books has become quite common. Some organizations, such as Google, have undertaken large projects to digitize entire libraries, if not the entirety of every book that has ever been written. Many books in the public domain, for instance, are available on archive.org. And more are available through Amazon and other digital book publishers. This shift is already having a profound impact on the publishing industry and the way people read books.
- Natural language processing and automation. One of the biggest use cases for OCR is within other automation tools that leverage NLP. NLP, which can process, “understand,” and generate human language, has an even wider set of use cases than OCR. These can include everything from chatbots to voice search to automatically analyzing the content of a text.
In and of itself, OCR is most frequently used for the first item on the list above – namely, digitizing records. And while it may have certain other peripheral use cases, it is most useful when combined with NLP, as we saw above.
NLP Technologies vs. OCR Technologies
NLP technology can be thought of as the next step up from OCR technology. While OCR only focuses on recognizing written characters, NLP actually processes language.
It performs tasks such as:
- Grammatical analysis. Tagging the parts of speech and understanding the grammatical structure of a piece of text.
- Text summarization. Rephrasing a long piece of text in a more concise form.
- Topic modeling. Extracting key ideas and topics from a text.
- Language generation. Creating new, human-sounding language from scratch.
- Sentiment analysis. Assessing the emotional content of a piece of text.
- Semantic analysis. Determining the meaning behind the words.
NLP techniques such as these can be combined into apps either simple or complex, such as:
- Chatbots. Chatbots or text user interfaces are commonly used in customer service or technical support, but they can also be used within digital workplaces for tasks such as employee training.
- Writing. Although AI writing abilities are quite limited, they are evolving rapidly. Language models such as GPT-3 have, for instance, been used to create language indistinguishable from human language.
- Voice user interfaces. Voice user interfaces are those used for apps such as Siri or Alexa. NLP performs several tasks to enable these apps, including voice recognition, grammatical analysis, semantic analysis, and more.
- Search engines. Search engines will often use a range of functions such as semantic analysis, sentiment analysis, and other NLP functions to optimize the search experience.
- Machine translation. Applications such as Google Translate will attempt to understand the meaning of a text and then translate that text into another language.
From this list we can see that one of the primary outcomes of NLP technologies and OCR technologies is automation.
Final Thoughts: OCR and NLP Are Just the Beginning
OCR technology and NLP technology are automating a number of tasks related to language. From reading to analyzing to generating new text, OCR and NLP technologies will result in automation tools that will have a profound impact on both business and our daily lives.
Today, we are only witnessing the beginning of these trends. In the years ahead, NLP and OCR technologies will both impact the workplace and society in ways that we probably cannot yet imagine.