A Bayesian Pairwise Classifier for Character Recognition

Abstract

In this chapter, we develop a Bayesian Pairwise Classifier framework that is suitable for pattern recognition problems involving a moderately large number of classes, and apply it to two character recognition datasets. A C class pattern recognition problem (e.g.; C = 26 for recognition of English Alphabet) is divided into a set of (2C) two-class problems. For each pair of classes, a Bayesian classifier based on a mixture of Gaussians (MOG) is used to model the probability density functions conditioned on a single feature. A forward feature selection algorithm is then used to grow the feature space, and an efficient technique is developed to obtain a MOG in the larger feature space from the MOG's in the smaller spaces. Apart from improvements in classification accuracy, the proposed architecture also provides valuable domain knowledge such as identifying what features are most important in separating a pair of characters, relative distance between any two characters, etc.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2001
Accession Number
ADA395393

Entities

People

  • Joydeep Ghosh
  • Melba Crawford
  • Shailesh Kumar

Organizations

  • University of Texas at Austin

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Character Recognition
  • Data Sets
  • Feature Extraction
  • Feature Selection
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Probability
  • Probability Density Functions
  • Random Variables

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space