A Novel Approach of Skew Normalization for Handwritten Text Lines and Words

Handwritten text recognition is an active research area for many years. Handwritten text recognition needs to perform some preprocessing steps for better recognition. Initially, we find binary image of given handwritten text document and then after performing the line segmentation task on handwritten text document, we have to normalize to the segmented lines. There are various normalization task we have to perform as skew normalization, slant normalization and size normalization. This paper, focuses on the skew normalization of handwritten text lines and we propose a new skew normalization approach which is based on orthogonal projection of the segmented line with respect to x-axis. The algorithm detects the exact skew angle, and corrects it efficiently. The method has been experimented on various text document images and achieves more than 98% accuracy. A comparative study has been reported to provide a detailed analysis of the proposed method together with some other existing methods in the literature.