Project and can also be applied at several sizes

Project Methodology Images are first converted into binary
format using Otsu thresholding algorithm. In writer identification, features do
not correspond to a single value, but a probability distribution function (PDF)
extracted from the handwriting images to characterize writer individuality. The
following features have been considered in this study. 1. Tortuosity: This feature makes it possible to
discriminate between fast writers who create smooth handwriting and slow
writers who create knotted handwriting. In the used dataset, for each pixel p
in the text, we consider 20-dimensional features related to tortuosity.
10-dimensional Probability density function represents the length of the
longest line segment which traverses p and completed within the text and
10-dimensional Probability density function denotes the direction of the
largest line portion. 2. Direction: This feature can measure the tangential
direction of central axis of text. Here it uses a Probability density function
of 10 dimensions. 3. Curvatures: This attribute is usually accepted in
forensic science examination which studies the curvature as discriminating
feature. It uses a Probability density function of 100 dimensions which
represent the values of curvature at the outline pixels. 4. Chain code: Chain codes can be generated by scanning
the outline of the text and assigning a number to each pixel according to its
location with respect to the previous pixel. For each pixel, we can consider
eight possible directions and consider the location with respect to the previous
1,2,3 and 4 pixels. 5. Edge direction: Edge-based directional features give a
detailed distribution of directions and can also be applied at several sizes by
positioning a window centered at each contour pixel and counting the
occurrences of each direction. This feature has been computed from size 1
(which Probability density function size is 4) to size 10 (which Probability density
function size is 40).  We will use K-Nearest neighborhood, L1
Regularized Logistic Regression, Decision tree, Random Forests and many new
algorithms to evaluate the image and predict the gender of the User. 

For the above-mentioned purpose, we will
use all the above attributes and will generate some new attribute to enhance
the efficiency of the existing system.

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