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Extracting Motion Primitives from Natural Handwriting Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationArtificial Neural Networks – ICANN 2006
EditorsStefanos Kollias, Andreas Stafylopatis, Włodzisław Duch, Erkki Oja
PublisherSpringer-Verlag Berlin Heidelberg
Pages634-643
ISBN (Print)978-3-540-38871-5, 978-3-540-38873-9
DOIs
Publication statusPublished - 2006

Publication series

NameLecture Notes in Computer Science
Volume4132
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Abstract

For the past 10 years it has become clear that biological movement is made up of sub-routine type blocks, or motor primitives, with a central controller timing the activation of these blocks, creating synergies of muscle activation. This paper shows that it is possible to use a factorial hidden Markov model to infer primitives in handwriting data. These primitives are not predefined in terms of location of occurrence within the handwriting, and they are not limited or defined by a particular character set. Also, the variation in the data can to a large extent be explained by timing variation in the triggering of the primitives. Once an appropriate set of primitives has been inferred, the characters can be represented as a set of timings of primitive activations, along with variances, giving a very compact representation of the character. Separating the motor system into a motor primitive part, and a timing control gives us a possible insight into how we might create scribbles on paper.

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