Optical character recognition technology has been around for quite some time, with most citing its origins as Emanuel Goldberg’s 1914 invention, which was developed as a tool for helping the blind read. Now, we can use lasers and computers to convert physical copies of text and images into digital forms that can then be adjusted within programs by humans. In fact, you can even convert JPGs to Excel sheets with the right OCR setup now by hiring an optical character recognition services.
As the physical worlds and digital worlds become more and more intertwined through the adoption of techs like live streaming and the goals of the Metaverse start to be fully realized, OCR tech looks to play an increasingly integral role. Perhaps, however, not exactly how you think it would. Already, in fact, OCR is playing a key and innovative role in many different sectors, and there are big plans to make it all the more integral going forward.
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Building to become a crucial technology
While the development of OCR tech began in the 1910s, one of the first major breakthroughs that led to the technology as we know it today took place in the 1970s. Ray Kurzweil created an omni-front OCR device that could read text in any font, and applied that to a machine that could read text and then present it in an audio format. Modern OCR applications can achieve this feat and so much more, with the discipline expanding into four different types of OCR.
Optical mark recognition is one of the most commonly used and one that just about everyone becomes subject to at some point. OMR identifies marks on tests and surveys to speed up the multiple-choice marking and collection of data. Similarly, but for qualitative data, optical word recognition scans typed text, while intelligent word recognition does the same for handwritten text.
Finally, intelligent character recognition advances optical character recognition into the field of comparison, such as by examining the different data points between two sets of handwritten characters – which proves useful for signatures, as an example.
In all of these formats, the core of the OCR tech is to convert an image of text into a format that the machine on the other side of the scan, be it a computer or small interface, can read. From there, the software can allow it to analyze the text to create an outcome, or filter it into a program that will allow the user to adjust or search through the text. When taking physical data into the digital world, OCR will only become more integral, especially when attached to deep learning applications of artificial intelligence.
Taking hardcopy, analog information from the real world into the digital will help to build our uses for physical information online and on computers. Still, computers and OCR tech can take this further, and certainly will do so with growing applications.
What’s needed is for the program on the other side of the OCR scan to be able to analyze, sort, and even learn from these scans, which is where deep learning can come into play as it derives meaning from text and data, building on the straightforward image-to-text applications.
Advanced applications of OCR
The reach of OCR technology as a reading device for images and text like barcodes and labels is perfectly demonstrated by the Dynamsoft range, which includes the Label Recognizer and the Barcode Reader.
The Label Recognizer, particularly, has a large scope of applications as it can read everything from alphanumeric to punctuation symbols, as well as text in different colors, for conversion in PDFs and PNGs. It also allows for reference regions to be created for speedier character recognition, such as with yellow boxes for price tags.
In a more subtle application of OCR for most of its users, it has been combined with live stream technologies to create a real-time, human-run game of live roulette online. Coming in the form of Dealers Club Roulette and the Real Roulette range with Rishi, Bailey, Sarati, and Holly, the roulette games take place in a studio with an OCR device placed over the roulette wheel. It tracks where the ball lands under its numbered pocket and relays that information to the control unit running the game digitally for online players in real time.
You could also look at the advancements in OCR technology being made by Hyperscience. By combining natural language processing, computer vision, machine learning, and their “intelligent” model of machine learning, the company has created a process that can automate the extraction of data from difficult-to-read documents.
While it does typically take longer to process, the advancements allow the product to be up to 99.5 percent accurate, customizable to just about any business process, and indicate when it thinks human intervention is required.
Optical character recognition technology is becoming increasingly useful and important in several sectors, from entertainment to supermarkets, and will only become more so as it assists the use of AI.
