Rough path models have also been used to analyse Intensive Care Unit data to identify those patients most likely to develop sepsis – a rapid onset condition with potentially devastating consequences. The handwriting interface is therefore an important tool allowing those with less digital confidence to access services and information through the Internet and now has around seventy-five million users a day. While most people in China prefer to use a digital keyboard, an estimated one hundred million people – many of them elderly and not city-based – still prefer to handwrite. Soon after, a leading company, developer of the popular Chinese ‘Pinyin’ input method editor, acquired access to the technology, releasing it to a wider audience through their market-leading ‘mobile keyboard’ for smartphones.
Gaining insights software#
The model was incorporated into a new version of the software and has now been downloaded by over a million users. The use of the rough paths model enabling effective character recognition in real time, and significantly improved the accuracy and speed of the app (as demonstrated in published articles). Prof Jin’s research group already had considerable experience with Chinese handwriting recognition and had developed the mobile phone keyboard app ‘gPen’ which translates handwritten characters into text. Later (2015), Lyons and his team team began to work with Professor Lianwen Jin from the Information Engineering Department at the Southern China University of Technology (SCUT), to use the model to analyse the pen strokes in Chinese characters. Putting two technologies together (signatures, and deep convolutional neural nets), Ben Graham won the ICDAR 2013 competition for recognising online Chinese characters. In 2012, Lyons started to interact with Ben Graham (then at University of Warwick, now at Facebook, and an early expert on deep neural networks). The rough path model effectively addresses these challenges.”
Many analysis techniques aren’t equipped to deal with them as they often treat each mode independently and can’t deal well with interactions between channels, randomness, or gaps in the data. Lyons comments: “Multimodal data streams are found in a huge range of situations and on all scales. This lead Lyons and his team to develop signatures into an effective tool to describe the interactions between complex data streams in data science. In a paper published in the Annals of Mathematics (2010), Professors Terry Lyons and Ben Hambly showed how ‘mathematical signatures’ from ‘rough path theory’ could be faithfully used to capture the key features of an evolving situation, capturing patterns and allowing accurate prediction and analysis. The methodology has been a key contributor to prize winning practical applications ranging from recognition of finger-drawn Chinese characters on mobile devices to analysing health data. GPen app being used to enter a Chinese characterEmanating from rough path theory, mathematical signatures, developed at the University of Oxford, have been combined with machine learning to enable lightweight, fast, and accurate recognition of complex and unpredictable data streams from different sources.
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