Reading Handwritten Scripts: Investigating the Human Visual System for Artificial Intelligence in Palaeography

With the renewed vitality of research in Artificial Intelligence, thanks in particular to the continued development of neural network techniques for machine learning, computer vision technologies developed for handwriting recognition offer innovative ways of conducting research in palaeography (Brusuelas, 2016; Hassner et al 2014; Muzurelle, 2011; Stutzmann, 2015) In this context where artificial intelligence often endeavours to replace human intelligence, or at least to emulate it, we are undertaking to understand better what it is that human intelligence does when reading ancient handwritten scripts. Ultimately, our ambition is to nudge artificial intelligence for palaeography to be intelligent enough to identify where human intelligence is still superior to machine intelligence (and therefore better left the upper hand) and where researchers can benefit from algorithmic support. Handwriting recognition is a challenging task – both for humans and machines – because handwritten scripts inherently straddle two interlinked spheres of intelligence: that of visual shapes, and that of semantics. This work builds on previous research (Terras, 2006; Youtie, 1963) that has identified six strands of human strategies in palaeography through ethnographic analysis, the results of which were crossreferenced with the cognitive sciences literature (Tarte, 2014). These strands were: visual perception, aural feedback, motor feedback, semantic memory, structural knowledge acquisition, and creativity; all continuously interacting with and feeding back into each other to some degree. In this project, we concentrate on the task of reading ancient handwriting – one of the many aspects of palaeographical research, whether it is concerned with mediaeval scripts or with more ancient scripts. In this paper, we present some findings and observations made in the process of designing experiments to investigate some of the mechanisms underlying handwriting recognition in a palaeographical context; preliminary results from the experiments themselves are forthcoming. To explore in depth how humans handle the variability of the shapes of signs in a given script, our experiments aim to bridge between traditional ethnographic methodologies, geared towards the gathering of qualitative data, and cognitive sciences methodologies, geared towards the gathering of quantitative data. The script of choice was Demotic, a script of the Late Egyptian language and whose use spanned ten centuries, therefore displaying a large variability in shapes. The team of scholars involved in designing and conducting our experiments was made of: an Egyptologist/Classicist, an Ethnographer, a Neuroscientist, and a Computer Scientist. Many of the observations reported here stem from the epistemological encounters of very different traditions of research; they emerged through the interdisciplinary conversations that took place in the process of designing the experiments. The outcome of these conversations was the following experimental setup, building on the principles of the protocols outlined by Althaus and Plunkett (2015) and Longcamp et al (2008).