I spent a lot of time when researching a PhD on Elizabethan politics and culture, focusing on an individual diplomat and politician called Robert Beale, looking at handwritten letters in various libraries or archives, mostly in the UK, but also in Europe and North America. I completed this PhD in 2000, but never got it published, as I had already started work in an internet advertising agency, and I then got married, started to have children and so tucked it all away in an archive of my own.
I’ve gone back to this material only recently, partly spurred on by a private desire to connect my academic past to my professional present and future, and also connected to in interest in applying Machine Learning or Artificial Intelligence tools to different domains or areas.
The emergence of ‘Deep Learning’ (more here later) in recent years as a technique to aid classification in a broad variety of areas or applications was something I, along with many others now, was familiar with. I was aware enough of the techniques involved to surmise that an application that recognised the identity of a handwriting sample, as well as ‘reading it’, should be something that would be possible.
The presentation here Mark Taviner Handwriting Identification and Applied AI Talk June 2015 is what I have written this month on this. The working title ‘Fingerprints in the Archives’ should be fairly obvious, and I reference the USA FBI NGI fingerprint system, as well as Facebook’s ‘DeepFace’ research in the talk.
Thanks to Professor Cathy Shrank for her support when I suggested this to her, and her video message on why this would be a valuable research tool for historical, cultural and literary research is hosted on Youtube here. I appreciate very much subsequent confirmation from Jeremy Howard at Enlitic.com that my ‘guess’ was correct and reference to a dissertation by Luiz Gustavo Hafemann that showed ‘state of the art’ results against a Brazilian Author Handwriting reference database.
There remains much work to be done…