i have to apply RANSAC algorithm on an image toget the best fit in license plate detection. how can it be possible?

function [bestModel,bestInliers,bestOutliers,bestError] = ransac(thresError,N,d,iterations)

syms inliers outliers ni no;

% model

bestModel = [];

bestInliers = [];

bestOutliers = [];

bestError = inf;

reply = input('Random generation (1) or input data (2)?\n');

if (reply == 1)

data= rand(2,N);

else

data = input('Enter the data matrix in 2xN format.\n');

end

for i=1:iterations

randomP = randperm(N);

p1 = [ data(1,randomP(1)), data(2,randomP(1)) ];

p2 = [ data(1,randomP(2)), data(2,randomP(2)) ];

inliers = [];

outliers = [];

ni = 0;

no = 0;

a_model = ( p1(2) - p2(2) )/ ( p1(1) - p2(1) );

b_model = p1(2) - a_model*p1(1);

totalError = 0;

for p=1:N

point = data(:,p);

error = abs( data(1,p)*a_model + b_model - data(2,p) )/ sqrt( a_model^2 + 1);

if( thresError < error )

outliers(:,no+1) = point;

no = no+1;

else

totalError = totalError + error;

inliers(:,ni+1) = point;

ni = ni+1;

end

end % data iteration end

% check model

if ( bestError > totalError && d < ni )

bestModel = [a_model,b_model];

bestInliers = inliers;

bestOutliers = outliers;

bestError = totalError;

end

end % main iteration end

return;

[ezhil K]

Even I have the same doubt.If you get a solution please share it with me.Plz