By Topic

HMM-Based Recognition of Online Handwritten Mathematical Symbols Using Segmental K-Means Initialization and a Modified Pen-Up/Down Feature

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Lei Hu ; Dept. of Comput. Sci., Rochester Inst. of Technol., Rochester, NY, USA ; Richard Zanibbi

This paper presents a recognition system based on Hidden Markov Model (HMM) for isolated online handwritten mathematical symbols. We design a continuous left to right HMM for each symbol class and use four online local features, including a new feature: normalized distance to stroke edge. A variant of segmental K-means is used to get initialization of the Gaussian Mixture Models' parameters which represent the observation probability distribution of the HMMs. The system obtains top-1 recognition rate of 82.9% and top-5 recognition rate of 97.8% on a dataset containing 20281 training samples and 2202 testing samples of 93 classes of symbols. For multi-stroke symbols, the top-1 recognition rate is 74.7% and the top-5 recognition rate is 95.5%. For single-stroke symbols, the top-1 recognition rate is 86.8% and the top-5 recognition rate is 98.9%. (MacLean et al., 2010) applied dynamic time warping algorithm on all the 70 classes of single-stroke symbols. Their top-1 recognition rate is 85.8%, and top-5 recognition rate is 97.0%. Our system gets top-1 recognition rate of 85.5% and top-5 recognition rate of 99.1% on the same 70 classes of single-stroke symbols.

Published in:

2011 International Conference on Document Analysis and Recognition

Date of Conference:

18-21 Sept. 2011