Collaborating Authors

Handwriting Recognition: Overviews

Machine Learning Framework Algorithm to recognise handwriting


Manually transcribing large amounts of handwritten data is an arduous process that's bound to be fraught with errors. Automated handwriting recognition can drastically cut down on the time required to transcribe large volumes of text, and also serve as a framework for developing future applications of machine learning. Handwritten character recognition is an ongoing field of research encompassing artificial intelligence, computer vision, and pattern recognition. An algorithm that performs handwriting recognition can acquire and detect characteristics from pictures, touch-screen devices and convert them to a machine-readable form. There are two basic types of handwriting recognition systems – online and offline.

A categorisation and implementation of digital pen features for behaviour characterisation Artificial Intelligence

The research described in this paper is motivated by the development of applications for the behaviour analysis of handwriting and sketch input. Our goal is to provide other researchers with a reproducible, categorised set of features that can be used for behaviour characterisation in different scenarios. We use the term feature to describe properties of strokes and gestures which can be calculated based on the raw sensor input from capture devices, such as digital pens or tablets. In this paper, a large number of features known from the literature are presented and categorised into different subsets.