Weighted PCA for improving Document Image Retrieval System based on keyword spotting accuracy

Feature weighting is a technique used to approximate the optimal degree of influence of individual features. This paper presents a feature weighting method for Document Image Retrieval System (DIRS) based on keyword spotting. In this method, we weight the features using Weighted Principal Component Analysis (PCA). The purpose of PCA is to reduce the dimensionality of the data space to the smaller intrinsic dimensionality of feature space (independent variables), which are needed to describe the data economically. This is the case when there is a strong correlation between variables. The aim of this paper is to show feature weighting effect on increasing the performance of DIRS. After applying the feature weighting method to DIRS the average precision is 92.1% and average recall become 97.7% respectively.