When you turn on your phone to take a picture of some text, maybe of a menu or newspaper, most modern smartphones provide the option to select or even search for the text in the image. This function to bring text from the real world into the digital world is a powerful tool often taken for granted. This function is known as Optical Character Recognition (aka. OCR).
OCR uses machine learning to analyze images, identify texts, and interpret fonts to generate digital text. Since most text is printed in contrast to its background, the software particularly pays attention to high-contrast areas to recognize text. The text in an image, especially printed text, can be easily identified by computers by matching fonts patterns to existing data; this process is known as "pattern matching". This works by isolating a specific character, a glyph, and comparing it with a similar glyph from pre-existing data to generate text.
Modern OCR is typical in healthcare, logistics, and banking industries. OCR helps to automate repetitive interpretations of text and can help save time and/or manpower when sorting documents.
The development of OCR technology
One of the earliest appearances of OCR technology was in the 1970s when the company "Kurzweil Computer Products Inc." developed the first omni-font OCR. This version of OCR could recognize printed text in virtually any font, using a process similar but less advanced to that of modern computers. This instance of OCR was used in reading machines for visually impaired people, where a flatbed scanner was connected to a text-to-speech synthesizer. Commercial OCR products officially became available in 1978, although many were slow due to the lack of computing power at the time. This generation of OCR technology also lacked the compatibility of modern-day OCR, as they were restricted to the use of the products themselves.
Advancements in machine learning and internet technology throughout the 2000s made OCR more accessible. The creation of WebOCR (OCR available as an online service) meant mobile and personal devices had access to OCR. This opened the gate of possibilities for products and applications such as real-time translation of foreign languages on smartphones. These products meant that OCR technology became much more accessible and available for anyone with the internet.
OCR Today
Breakthroughs in machine learning and neural networks meant a boost in the reliability and efficiency of OCR technology. With convolutional neural networks (CNNs) and recurrent neural networks (RNNs), OCR technology has become more accurate. It can expand toward identifying characters from languages with intricate scripts, such as Middle Eastern Languages.
Recent innovations expand OCR beyond traditional uses of text recognition. With the integration of other machine-learning-based technologies, such as natural language processing, OCR technology can be expanded for uses such as processing handwritten notes. These innovations allow for OCR to be integrated into more areas of society, and with the advent of open-source OCR projects in various languages, the uses of OCR will significantly increase as more innovations arise. The emergence of OCR technology in tasks such as document classification, intelligent document processing, and text recognition in augmented reality applications provides flexible implications for OCR.
Conclusion
OCR technology has significantly improved since its original usage of reading machines for visually impaired personnel and has since profoundly integrated into people's daily lives. The rise of OCR technology has transformative impacts on document digitization and data extraction, being significantly used in archiving. The exploration of OCR with the current explosion in AI technology will also increase the variety of uses for OCR and how OCR could be further integrated for consumers' convenience. Next time you open up your camera to copy text, take a moment to think about all the machine learning of OCR technology.
Reference List
Amazon Web Services, Inc. (2024). What is OCR? - Optical Character Recognition Explained - AWS. [online] Available at: https://aws.amazon.com/what-is/ocr/ [Accessed 28 Feb. 2024].
Engels, A. (2022). This scanner app with OCR is maybe the best deal you can get today. [online] nextpit. Available at: https://www.nextpit.com/mobile-doc-scanner-app-free-limited-time [Accessed 28 Feb. 2024].
MOS (2023). 8 Industries that Benefit from Optical Character Recognition. [online] Managed Outsource Solutions. Available at: https://www.managedoutsource.com/blog/top-uses-optical-character-recognition/ [Accessed 28 Feb. 2024].
Comments