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Event Details
Event Name: GCCIS Ph.D. Student Dissertation Proposal Defense
Category: Academic/College Events
Sub Category: GCCIS
Description: Structured Learning Applied to Recognition of Handwritten Math Expression
Lei Hu

Handwritten math expression recognition can benefit human computer interaction especially in the education domain and is a very important part of document recognition and analysis. Structured learning is the subfield of machine learning and has attracted extensive attention recently. In contrast with conventional supervised learning such as classification and regression, structured learning is concerned with algorithms that learn to map input data to structured outputs (graphs).

Math expression is structured data which consists of several parts, and not only the parts themselves (symbol labels) contain information, but also the way in which the parts belong together (spatial relations). Math expression recognition includes three major steps: symbol segmentation, symbol recognition and layout analysis. This research is focusing on applying structured learning to jointly address symbol segmentation, classification and layout analysis more efficiently and have better generalization than current stochastic context free grammar driven systems for handwritten math recognition.

Lei Hu is a 4th year Ph.D. student in the College of Computing and Information Sciences at RIT, working under the supervision of Dr. Richard Zanibbi. His research interests include pattern recognition, machine learning and document analysis.
Schedules: 11/18/2013   (3:00 PM - 4:00 PM)

Contact: Joyce Hart
Phone: 475-6193
Cost: Cost - Free