Text Classification via iVector Based Feature Representation

Abstract

In this paper, we address the problem of text classification: classifying modern machine-printed text, handwritten text and historical typewritten text from degraded noisy documents. We propose a novel text classification approach based on iVector, a newly developed concept in speaker verification. To a given text line, the iVector is a fixed-length feature vector representation, transformed from a high-dimensional super vector based on means of Gaussian mixture model (GMM), where the text dependent component is separated from a universal background model (UBM) and can be represented by a low dimensional set of factors. We classify the text lines with a discriminative classifier - support vector machine (SVM) in iVector space. A baseline approach of text classification using GMM in feature space is also presented for evaluation purpose. Experimental results on an Arabic document database show accuracy of 92.04% for text line classification using the proposed method. Furthermore, the relative word error rate (WER) of 9.6% is decreased in optical character recognition (OCR) when coupled with the proposed iVector-SVM classifier. The proposed iVector-SVM approach is language independent, thus, can be applied to other scripts as well.