Signature detection and segmentation is a known field of study and techniques involve shape matching. While some of this processing involve offline techniques, there are online techniques also mentioned in the associated literature.  Moreover, MYCT-Signature corpus, Susig database and GPDS-960 provide well known databases for evaluating algorithms.  For example, one method of non-rigid shape matching involves a spatial histogram aka shape context computed for each point which describes the distributions of the relative positions of all remaining points. The correspondences between points are solved through weighted bipartite graph matching before the signatures are matched. Another method of non-rigid shape matching formulates it as an optimization problem that preserves a local neighborhood structure. This method has an intuitive graph matching interpretation where each point represents a vertex and two vertices are considered connected in the graph if they are neighbors. The problem of finding optimal match between shapes is therefore equivalent to maximizing the number of matched edges between their corresponding graphs under a one-to-one matching constraint. In this optimization approach, an iterative framework is used  to estimate the correspondences and the transformation. In each iteration, graph matching is initialized using shape context distance and subsequently updated through relaxation labeling which is a well-known formal method of expressing low level contextual information, and applying it to complete the extraction of image features. 
Image processing generally involves multiple subsequent stages of processing the images. Signatures have the nice property that they are like the results of sobel edge detection and the edges are expected to be more continuous in their formation.  Moreover, signature pads are  small images, with similar curves and accents and purely black and white, so they are near consistent and this helps with their processing. 
#codingexercise
A person wants to form teams by selecting as many participants from a list as possible. The participants have skills represented by an integer. The skills selected as such must be distinct and contiguous even if they are negative. By making the team as large as possible, more problems can be solved. What is the size of the team he can form ?
one way to do this would be to sort the skills and find the largest distinct unit incremental subsequence.

Another way to do this is with longest increasing sequence.
Int GetLongestIncreasingSubsequence(List<int> A)
{
var best = new int[A.Length+1];

for (int i = 0; i < best.Length; i++)

       best[i] = 1;

for (int i = 1; i < A.Length; i++)

    for (int j=0; j < i; j++)

         if (A[i] == A[j] + 1)

          {

               best[i] = Math.Max(best[i], best[j]+1);

          }
return best.ToList().max();
}

The above assumes distinct elements.

another exercise

A person wants to buy L items from her favorite store such that a subset of N items must contain D distinct items.  the items range from 1 to A in price. Determine the maximum amount of money the person can spend.

Since the price has to be maximized, the algorithm has to be greedy in its strategy to select the next item. when we can no longer purchase the highest priced item because it violates the given restriction, we make the subsequent selection  from the next lower priced item. we determine the threshold from the range 1 to n/d. The rest is recursive combination as shown earlier.

Courtesy: hackerrank