A New COLD Feature based Handwriting Analysis for Ethnicity/Nationality Identification

TL;DR

This paper introduces a novel handwriting analysis method using Cloud of Line Distribution (COLD) features to identify ethnicity or nationality, leveraging contour and line segment analysis with SVM classification.

Contribution

The paper presents a new COLD-based feature extraction technique for handwriting analysis that improves ethnicity and nationality identification accuracy over existing methods.

Findings

Method outperforms existing approaches on complex datasets.

COLD features effectively capture shape variations related to nationality.

High classification accuracy achieved with SVM classifier.

Abstract

Identifying crime for forensic investigating teams when crimes involve people of different nationals is challenging. This paper proposes a new method for ethnicity (nationality) identification based on Cloud of Line Distribution (COLD) features of handwriting components. The proposed method, at first, explores tangent angle for the contour pixels in each row and the mean of intensity values of each row in an image for segmenting text lines. For segmented text lines, we use tangent angle and direction of base lines to remove rule lines in the image. We use polygonal approximation for finding dominant points for contours of edge components. Then the proposed method connects the nearest dominant points of every dominant point, which results in line segments of dominant point pairs. For each line segment, the proposed method estimates angle and length, which gives a point in polar domain. For all the line segments, the proposed method generates dense points in polar domain, which results in COLD distribution. As character component shapes change, according to nationals, the shape of the distribution changes. This observation is extracted based on distance from pixels of distribution to Principal Axis of the distribution. Then the features are subjected to an SVM classifier for identifying nationals. Experiments are conducted on a complex dataset, which show the proposed method is effective and outperforms the existing method