Skew Estimation for Unconstrained Handwritten Documents

Abstract

Document skew estimation is one of the most important and challenging phase in OCR system. Skew estimation of handwritten documents is still remains challenging in the field of document image analysis due to a non-uniform text line. Hence, in this paper, we present a novel scheme for handwritten documents. The proposed method is based on mixture models. The expectation-maximization (EM) algorithm is used to learn the mixture of Gaussians. Subsequently the cluster means obtained from the individual words is used estimate the skew angle. Experiments on different handwritten documents and documents corrupted by noise shows the effectiveness of the proposed method.