Edinburgh Research Explorer

A Primitive Based Generative Model to Infer Timing Information in Unpartitioned Handwriting Data

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

Original languageEnglish
Title of host publicationIJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007
EditorsMM Veloso
Place of PublicationFreiburg
Number of pages6
Publication statusPublished - 2007
Event20th International Joint Conference on Artificial Intelligence - Hyderabad, India
Duration: 6 Jan 200712 Jan 2007


Conference20th International Joint Conference on Artificial Intelligence


Biological movement control and planning is based upon motor primitives. In our approach, we presume that each motor primitive takes responsibility for controlling a small sub-block of motion, containing coherent muscle activation outputs. A central timing controller cues these subroutines of movement, creating complete movement strategies that are built up by overlaying primitives, thus creating synergies of muscle activation. This partitioning allows the movement to be defined by a sparse code representing the timing of primitive activations. This paper shows that it is possible to use a factorial hidden Markov model to infer primitives in handwriting data. The variation in the handwriting 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. The model is naturally partitioned into a low level primitive output stage, and a top-down primitive timing stage. This partitioning gives us an insight into behaviours such as scribbling, and what is learnt in order to write a new character.


20th International Joint Conference on Artificial Intelligence


Hyderabad, India

Event: Conference

Download statistics

No data available

ID: 18651066