Measuring and optimising performance of an offline text writer identification system in terms of dimensionality reduction techniques

Abstract

Usually, most of the data generated in real-world such as images, speech signals, or fMRI scans has a high dimensionality. Therefore, dimensionality reduction techniques can be used to reduce the number of variables in that data and then the system performance can be improved. Because the processing of the high dimensional data leads the increase of complexity both in execution time and memory usage. In the previous work, we developed an offline writer identification system using a combination of Oriented Basic Image features (OBI) and the concept of graphemes codebook. In order to measure and optimise the system performance, a variety of nonlinear dimensionality reduction algorithms such as Kernel Principal Component Analysis (KPCA), Isomap, Locally linear embedding (LLE), Hessian LLE and Laplacian Eigenmaps have been used. The performance has been evaluated based on IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting. The results obtained indicated the system performance based KPCA was better than the other reduction techniques that have been used and investigated in this work.