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Details of Grant 

EPSRC Reference: EP/J021458/1
Title: Learning Models of Handwriting for Structured Texture Synthesis
Principal Investigator: Brostow, Professor GJ
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Computer Science
Organisation: UCL
Scheme: First Grant - Revised 2009
Starts: 21 January 2013 Ends: 20 January 2014 Value (£): 99,142
EPSRC Research Topic Classifications:
Computer Graphics & Visual. Image & Vision Computing
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
06 Jun 2012 EPSRC ICT Responsive Mode - Jun 2012 Announced
Summary on Grant Application Form
The proposed research aims to produce images of handwriting using a new generative mathematical model of different people's handwriting styles and the specified text strings to be "forged." Our model uses image-fragments of real handwriting samples and learns what fragments are compatible under what deformations.

Our intended analysis/synthesis applications include quantitative authentication of handwriting in legal cases, inpainting of destroyed sections of historical documents, imitation of handwriting for banking purposes, and eventually, writer-specific handwriting recognition and drawn-sketch interpretation.

The specific objectives of this project are to:

- Automatically synthesize handwriting of novel text but in a particular person's style.

- Measure the likelihood that a given sample was written by a specific individual.

- Build a system that suggests what examples of handwriting should be captured next.

The project scope is limited to just handwriting to assess feasibility. At its conclusion, we expect to further pursue the following additional objectives:

- Demonstrate the synthesis and analysis approaches on large-scale forgery studies. The goal is to measure our forgery and forgery-detection rates to help transfer these techniques into industrial practice. Abuse of the technology for forgery creation could be limited by filming an individual while writing, to confirm that the synthesis was not automatic.

- Build a similar adaptive authoring system to help synthesize cartoon animation. Replace strings of characters used in the present project with a structure based on limb-configurations (2D joint-angles) of a stick-figure.

- Explore a unified framework for example-based generative models. In many domains, better synthesis should be possible when multiple examples are available. Findings from this near-term research on handwriting should generalize, so that structured acquisition of training examples steadily improves the quality of synthesized drawings, 3D shapes, and video post-production.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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