Imense (AI Company)

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Imense (AI Company)
Private
IndustrySoftware, Embedded
Founded2004; 18 years ago (2004) in Cambridge, UK
FounderChristopher Town and David Sinclair
Headquarters
Cambridge
,
UK
Key people
Dr. David Sinclair, Dr. Christopher Town, Henrik Kjellin
ProductsComputer Vision and OCR technologies
Websiteimense.com

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.

History

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.

Launched products

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.

Research focus

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.

References

External links

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