Nope.

You need to search on the web for "Optical Character Recognition". The results can be quite disappointing though. When was the last time you bought a web cam that can convert some text on paper into a Word document The problem is that even on an 360x440 image you've got to wade through 158,400 pixels to see if there's anything sensible there.

Also look up filters - you'll need them.

Your best best if I haven't put you off is to look on the net for Java projects that do what you want. You can still import them into Processing, or failing that ask the board about how to integrate a particular Java library in Processing.

Here's a start:

Somebody's OCR project

Sounds like what you might need is to roll your own neural network.  You're kind of in luck - http://www.jooneworld.com/ is the home of Joone, an open source Java NN package.  The downside?  Unless you're somewhat (very?) experienced with using neural networks effectively, you're never going to be able to use this to do what you want.  One of the introductory examples they give with that package is using it to perform handwriting OCR, but I'll warn you, it doesn't really work so well, and that example only handles very low resolution images (I think it's 9x9 or something like that).  But at least in theory, their package can do what you want, it would just take a whole lot of work.

My suggestion is that if you do try to do this, you'll need at least one preprocessing step which picks out each character and makes sure it's oriented correctly.  Beyond this, I really hope that you're dealing primarily with a single font and not with handwriting, because that constrains the types of tough calls your program will need to make.

Then again, if you _are_ just dealing with a single font of a fixed size, you may have better luck storing an image of each pixel, getting each character, and rotating/shifting/scaling it until you get a probable match (which you can tell because most of the pixels will line up).  Still, though, this is a very tough problem, so be prepared for a lot of work ahead.

thanks folks for all the wonderful pointers.  I have also found a simple javaNN called generation5 to try it out on OCR, it has a simple example of recognizing digits of 5x7 pixels using 35 perceptrons.  I need to try it out on my characters which are more in the 30x30 pixels range.  Is there any reason perceptrons would not work well on higher resolution images?  don't I simply scale it up to 900 perceptrons?  st33d mentioned that nn would not work well on larger sets of data, why?

it seems that single layered perceptron will do the job.  It takes very little time to train and recognize!
http://www.generation5.org/content/2004/simple_ocr.asp

First, I agree with all of st33d's posts 100% - neural nets are no magic bullet here, but they can help as a fallback if you can't figure the character out using other means.

To understand why higher resolution data is difficult to use with neural networks, consider this: a neural net with M input nodes, N intermediate nodes, and O output nodes has MxN+NxO connection weights to train.  That means you have a whole crapload of parameters to vary, and the more parameters you have to set, the more independent input data you need.  The problem?  Think of polynomial curves - a N-th degree polynomial can exactly fit N+1 data points, though the resulting polynomial will tell you nothing at all about the underlying pattern in the points.   This phenomenon is known as overfitting, and is especially prevalent in neural networks because the number of connections is so huge.

Doubling the resolution of an image multiplies the number of pixels by 4, so quickly pushes up the number of input nodes.  To get anything out of these, you'll likely have to increase the number of hidden nodes, so the number of connections to train increases VERY quickly.  To get meaningful results, this forces you to increase the training set even more dramatically, and if I recall, the total training time increases even faster than any of these values.  So that's why it's not easy to scale - you're dealing with an O(N^p) process where p is some fairly large number.

From what I remember, sometimes Kohonen or RBF nets are well suited to classification tasks, and I think these scale a bit better than your standard feedforward one, so you may give a look there.

Also, if you need to take high-res input, consider filtering it down to a lower, more usable resolution first, perhaps several different times in several different ways.  If your classifier gives the same answer on each different filtering, you can probably be fairly confident that it's correct; if the predicted character differs, this may be an indication that the confidence level is fairly low.

One other thing I would recommend - if you're dealing exclusively with English text, for instance, you might consider recognizing characters in context, for instance if the last three letters were 't,' 'h,' and 'i,' there's a much higher probability that the next character will be 's' than 'a,' given the 4-character probabilities for English text (though if the next letter is 'm,' you might have "thiamine," so the possibility of 'a' has to be kept open...).  You can often make do with a less accurate character recognizer if you employ tricks like that to post-filter your strings, though robust implementations of these types of tricks can be difficult.

ewjordan thinks you are using a back propagation neural net, which uses three layers of interconnected perceptrons. He hasn't guessed that you might be using a single layer of perceptrons.

The purpose of using multiple layers is that it can recognise situations a single layer can't.

But what ewjordan was saying about the noise is true. You're taking a tool that tries to deal with a lot of confusing information. But instead of making it easier for that tool to do the job, you are taking the piss out of it.

I'm pretty doubtful it will work (or at least it will appear to work and then you'll find it can only recognise three letters). But I've no hard evidence other than that every time I've tried to teach a neural net more patterns, it gets worse at it's job. So I say go for it. Prove me wrong and add to my research.

I take it you haven't tried if you're still asking questions. Come on then!