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Optical Character Recognition (OCR) is a fascinating technology that’s slowly but surely redefined the way people interact with physical information like handwritten or printed text. The power of OCR is in its transformation of physical information into digital data which can be quickly and securely translated, saved, and archived.
The technology has been a game-changing tool to improve accessibility and efficiency across a broad spectrum of applications. The concept itself is more than a century old. The first known example of someone using technology to recognize text was in the 1910s, when Emanuel Goldberg’s Statistical Machine was developed to understand characters and replicate them using telegraphic code.
Introducing the First Iteration of Optical Readers
Nevertheless, it wasn’t for another 40–50 years that the possibilities of OCR would be fully realized. An American inventor named David H. Shepard constructed a tool he labelled a “Gismo”, capable of understanding text printed in certain fonts. Shepard blazed a trail for a technology which would move at a rapid pace through the 1950s and into the 60s, when commercial-scale OCR solutions came into use. The IBM 1962 and the IBM 1287 were both able to understand various texts and convert it into a machine-readable format. Shepard would go on to innovate the world’s inaugural voice recognition software, too.
The IBM 1287 could take one look at printed text or documents cut to form and replicate the data for a compatible IBM CPU. OCR technology continued to take big strides forward in the subsequent decades, with these optical readers soon capable of interpreting handwritten text as well as typed text. It was also engineered to recognize less common font types. All of which helped OCR technology to become a more versatile solution, capable of entering the mainstream and bridging the gap between the physical world and the increasingly digital world.
Of course, the rapid proliferation of OCR solutions wouldn’t have been possible if it wasn’t for the emergence of more intelligent algorithms and quicker CPUs. All of which allowed end users to begin to digitize printed data en-masse. Around the era of the “Dotcom Boom”, there was a spin-off of intelligent character recognition (ICR), capable of understanding and replicating content in more sophisticated on-page layouts and even more complex handwriting.
Other Modern Use Cases of OCR Technology
Businesses of all shapes and sizes increasingly utilize OCR as a document management solution. This is across all manner of professions, including legal and medical industries, where paperwork volumes are highest. By digitizing documents, firms can easily retrieve sensitive information.
In the iGaming industry, which has warmly embraced real-time live dealer casino games, OCR is used within live casino studios to improve the speed of gameplay. OCR cameras pick up the results of table games like roulette spins and blackjack deals before quickly relaying them to online players via their desktop, laptop, or mobile screens. The leading iGaming operators now have dedicated live casino game libraries separate from their slots and table games powered by random number generators (RNGs). They even cover localized games tailored to each regulated audience, including Sic Bo, Fan Tan, and many other specialist regional games. Users in permitted Canadian provinces can play the best live casino on Casino Days website, featuring many of these iconic games that have been enjoyed by multiple generations.
In the worlds of finance and healthcare, OCR also plays an integral role in administrative tasks. It can automate data entry, minimize the risk of human error, speed up payment processes, and digitize client and patient records, all while maintaining compliance regarding data privacy and security.
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In the 21st century, the latest OCR technology has been further aided by the adoption of machine learning and AI to improve the accuracy and efficiency of digitization. So much so that the British Library, home to the Magna Carta and one of the world’s largest libraries, adopted OCR to protect the integrity of historic documents and create new, digital collections.
With machine learning and AI expected to weave itself more into the fabric of OCR solutions, it’s likely the technology will offer even greater productivity benefits, opening the door to new avenues for data exploration and automation.