1 research outputs found
A New COLD Feature based Handwriting Analysis for Ethnicity/Nationality Identification
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 methodComment: Accepted in ICFHR1