Tuesday, November 8, 2016

Handwriting Analysis

Have you ever struggled to read someones handwriting? That seems to be a classic problem, if someone is only writing for themselves, their handwriting seems to become nearly illegible to anyone else. Yet, they can still read it perfectly fine. So if you were to get someone else's grocery list, would you be able to go get everything they need? Better still, what if you had an app that could make sure that you got everything on their list. Think about being able to scan your notes into an editable format, would that be very useful? How about possibly detecting when someone attempts to fake your signature? There are many interesting applications for handwriting recognition. This technology would be useful to have in society.

Handwriting recognition comes in two flavors, on line and off line. On line refers to any handwriting being directly inputted into a system, off line refers to the analysis of a photography of handwriting. The previous example of a grocery list would be off-line, where as signing the credit card system at the store would be an example of on line. The benefits off on line is that you have more situational data to analyze, length of contact with the screen, amount of pressure applied etc. Obviously the downside to this is that there is more to keep track of.

A straightforward way to tackle this problem is simplification, attempting to identify one character at a time. After that, simple learning techniques like k-nearest neighbors will work. It is important to specify what domain is being used for this technique, i.e. Roman alphabet of Modern Standard Arabic, the more you are able to reduce the domain the more success this technique enjoys.

A more common way to analyze handwriting is through training of a neural network based off of several handwriting characteristics. It is important when deciding what to represent in your vector as a characteristic of handwriting. Some things such as aspect ratio, curvature and location relevant to other letters. You can see how breaking down a problem into its fundamental aspects is a property of learning. How you choose to break down the information will determine the overall classification process and therefore your results. There are even some crazy people attempting to use unsupervised learning to classify handwriting, to varying degrees of success.

Something that is very cool, is that actually have competitions for this, whose algorithms can identify handwriting the best. There is a conference called ICFHR (International Conference on Frontiers in Handwriting Recognition) that holds a annual competition. The examples they give for pages that will be analyzed (the actual pages themselves are kept secret till the competition) actually look very impressive. I couldn’t understand anything from just trying to eyeball it, and shows that in practice it is possible for a computer to read better then a human.

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