Supervised Discrimination of Characters on Images

Authors

Thibault Deutsch

Type

techreport

Year

2016

Number

1611

Abstract

The discrimination of characters is an important domain of optical characters recognition. The goal is to determine if a delimited surface of an image is a character or not, with rotation invariance. We are able to reduce the redundant information by doing a principal component analysis (PCA) on the training data set. Then, we use the probabilistic linear discriminant analysis (PLDA) algorithm to models both intra-class and inter-class variance as mutli-dimensional Gaussians. The performance of the new model will be compared with the one currently used in the optical characters recognition application of Olena.