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 …

Date

2014

Authors

Shengxin Zha, Xujun Peng, Huaigu Cao, Xiandan Zhuang, Pradeep Natarajan, Prem Natarajan

Conference

2014 11th IAPR International Workshop on Document Analysis Systems

Pages

151-155

Publisher

IEEE