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Optical Character Recognition: Traditional vs. AI-Powered


Optical character recognition (OCR) is a technology that recognizes text within digital images. It is mainly used to recognize text in scanned documents and images. Lately, companies have been using this intelligent system in collaboration with artificial intelligence to automate workflows and manage documents. In earlier articles, we learned all about artificial intelligence, robotic process automations, and how they work. Now, we can see that they also work in conjunction with older technologies to improve their abilities.


As mentioned before, optical character recognition is a technology that specializes in recognizing and classifying the text characters within an image, whether it be photos or scanned documents. OCR then converts the text into characters that computers can read as data. With this transformation to computer-readable text, the characters are now usable across word- and data-processing platforms. Optical character recognition is all around us, and has been for some time. You may have even used an OCR without knowing it – one example is the image-reading feature of Google Translate.

A main modern issue with traditional optical character recognition is that its ability is limited. If characters are written sloppily or are hard to read on a page, an OCR software might not be able to pickup on this tricky text, and may completely forget to extract such data. This is where artificial intelligence can step in and utilize machine learning processes to deduce that there is text where an old-fashioned OCR might not be able to do so. In short, artificial intelligence helps optical character recognition software in the areas of data structure and data capture.


Despite all the good that traditional optical character recognition has done over the years, its limitations are forcing software developers to find ways to improve its accuracy. Traditional OCR alone is not efficient enough for the modern business world. The improvements that AI has provided for OCR have allowed for easier data structuring and capturing across every industry that OCR is used in. With the ability to identify patterns and logic in unstructured and uncategorized data, machine learning methodologies add a crucial missing feature to OCR systems.

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