Research Interests
Broadly speaking I'm interested in harnessing machine learning
algorithms and related probabilistic techniques to aid in solving
real-world problems.
Most of my work thus far has focused on aspects of document image
analysis. Earlier research involved the application of various
sequential learning algorithms to carry out page segmentation and
region labelling tasks. My MSc thesis work has focused on font
independent approaches to optical character recognition that take a
top-down, code-breaking style attack to discern symbol labels
(completely ignoring glyph shape and font during recognition).
- Shape-Free Statistical Information in Optical Character
Recognition
Scott Leishman (2007).
Master's Thesis, University of Toronto
Available here: PDF (6.4mb)
-
A Statistical Learning Approach To Document Image Analysis
Kevin Laven, Scott Leishman, Sam Roweis (2005).
8th International Conference on Document Analysis and Recognition
(ICDAR 2005) pp 357-361
Available here: PDF (376kb)
-
Keyboard Acoustic Emanations
Machine Learning Tea Talk, University of Toronto
May 3rd, 2006
Available here: PDF (1.1mb)
-
Cryptogram Decoding for Optical Character Recognition
2006 CIAR
NCAP
Summer School
August 6th, 2006
Available here: PDF (1.7mb)
Other talk slides: here
-
The Integral Image Trick for Efficient Rectangular Sum and Area
Estimation
Machine Learning Tea Talk, University of Toronto
March 28th, 2007
-
Shape-Free Statistical Information in Optical Character
Recognition
Machine Learning Seminar, University of Toronto
April 2nd, 2007
Abstract
Talk slides here: PDF (2.5mb)
Collaborators
- Sam Roweis:
He's my current supervisor, and collaborator on JTAG
- Kevin Adam Laven: We both did work on JTAG, which he later used in
his MSc thesis