An Improved Method for Handwritten Document Analysis Using Segmentation, Baseline Recognition and Writing Pressure Detection

Abstract

Handwritten document analysis is a scientific technique for identifying and understanding the personality of a writer through the strokes and patterns revealed by writer's handwriting. This research proposed an off-line handwritten document analysis through segmentation, skew recognition and writing pressure detection for cursive handwritten document. The proposed segmentation method is based on modified horizontal and vertical projection that can segment the text lines and words even if the presence of overlapped and multi-skewed text lines. Proposed work also present orthogonal projection based baseline recognition and normalization method as well as writing pressure detection method that can predict the personality of a writer from the baseline and writing pressure. The proposed method was tested on more than 550 text images of IAM database and sample handwriting image which are written by the different writer on the different background. The proposed method also provides a comparative study of the details analysis of the proposed method with other existing methods.