Private | |
Industry | Software, Embedded |
Founded | 2004 | in Cambridge, UK
Founder | Christopher Town and David Sinclair |
Headquarters | Cambridge , UK |
Key people | Dr. David Sinclair, Dr. Christopher Town, Henrik Kjellin |
Products | Computer Vision and OCR technologies |
Website | imense |
Imense is a British[1] AI (artificial intelligence) company that develops and licenses computer vision OCR technology for use on phones, mobile and embedded devices, in server environments, and as client side modules in web pages.
Imense was founded through the lean startup process in 2004 by Christopher Town and David Sinclair with the intent of commercially realising semantically rich visual content based image recognition and retrieval technology. Series A funding was raised in 2007 to support the development of the first symbolically grounded image retrieval language.[2]
Imense was one of the pioneers of the application of large scale machine learning and grid computing (a precursor to cloud computing) technologies to image analysis and image content understanding.[3]
Expertise in creating highly efficient visual classifiers led to the development of fast and accurate OCR (optical character recognition) technology well suited to applications like automated number plate recognition (ANPR/ALPR), ID document verification, VIN (vehicle identification number) reading, handwritten form or label digitisation, and traffic sign classification for autonomous vehicles.
Imense has launched a wide range of automatic license plate recognition (ANPR/ALPR) and ID verification SDKs and apps for Android, iOS, Linux, Windows and HTML5 web modules. Application domains include security, surveillance, parking and traffic management, enforcement of speed restrictions, check-in automation, and document ID verification.
Imense's research focus is on building classes of visual features that can be computed efficiently and carry enough information to allow them to be semantically labelled either in isolation or as part of a visual tableau or temporal stream.
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