Georgetown Law
Technology Review

Image recognition technologies (IRTs) are tools that are capable of taking images, detecting and classifying objects within those images, and outputting some desired result based on the analysis. With computers becoming more powerful and image data availability increasing, IRTs are continually improving, enabling humans to use them for applications like optical character recognition (OCR), healthcare, and facial recognition.1See, e.g., Naveen Joshi, The Present and Future of Computer Vision, Forbes (June 26, 2019, 11:19 PM), https://www.forbes.com/sites/cognitiveworld/2019/06/26/the-present-and-future-of-computer-vision/#26cba0f6517d [https://perma.cc/QT7X-DZWF]; Kaz Jaszczak, Optical Character Recognition: A Backbone for Postal and Mail Sorting Applications, Mailing Systems Tech. (Apr. 7, 2011, 10:18 AM), https://mailingsystemstechnology.com/article-2813-Optical-Character-Recognition-A-Backbone-for-Postal-and-Mail-Sorting-Applications.html [https://perma.cc/DQL7-CLNA]. IRTs are numerous and complex, and they have a growing presence in the image recognition market.2See, e.g., Image Recognition Applications: 7 Essential Future Uses, Data Science Society, https://www.datasciencesociety.net/image-recognition-applications-7-essential-future-uses/ [https://perma.cc/3SAD-G7BB]. See generally Joshi, supra note 1. A brief overview of IRTs is provided, followed by an introduction to the machine learning tools used to implement IRTs, and an overview of their advantages and disadvantages.

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