Leave-One-Out-Training and Leave-One-Out-Testing Hidden Markov Models for a Handwritten Numeral Recognizer: The Implications of a Single Classifier and Multiple Classifications

Authors:
Albert Hung-Ren Ko;Paulo Cavalin;Robert Sabourin;Alceu de Souza Britto, Jr.
Affiliations:
University of Toronto, Toronto;Génie de la Production Automatisée (GPA), Montréal;École de Technologie Supérieure, Montréal;Informática Aplicada (PPGIa-PUCPR), Curitiba
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2009

Citing 0
Cited 2

LoGID: An adaptive framework combining local and global incremental learning for dynamic selection of ensembles of HMMs

Pattern Recognition
Single Classifier-based Multiple Classification Scheme for weak classifiers: An experimental comparison

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.15

Visualization

Abstract

Hidden Markov Models (HMMs) have been shown to be useful in handwritten pattern recognition. However, owing to their fundamental structure, they have little resistance to unexpected noise among observation sequences. In other words, unexpected noise in a sequence might “ break” the normal transmission of states for this sequence, making it unrecognizable to trained models. To resolve this problem, we propose a leave-one-out-training strategy, which will make the models more robust. We also propose a leave-one-out-testing method, which will compensate for some of the negative effects of this noise. The latter is actually an example of a system with a single classifier and multiple classifications. Compared with the 98.00 percent accuracy of the benchmark HMMs, the new system achieves a 98.88 percent accuracy rate on handwritten digits.