Computer handwriting recognition is very cool by itself, and it’s something that we’d like to incorporate into a project. So we went digging for hacker solutions, and along the way came up with an interesting bit of history and some great algorithms. We feel like we’ve got a good start on that front, but we’re stuck on the hardware tablet sensor itself. So in this Ask Hackaday, we’re going to make the case for why you could be using a tablet-like device for capturing user input or doing handwriting recognition, and then we’re going to ask if you know of any good DIY tablet designs to make it work.
They say your handwriting is as unique to you as is your fingerprint. Maybe they are right – perhaps every person adds a little bit of his or her personality to their penmanship. Just maybe there are enough ways to vary pressure, speed, stroke, and a dozen other almost imperceptible factors that all 7 billion of us have a slightly different style.
The study of handwriting is called Graphology, and people have been at it for a quite a long time. Most experts agree that a person’s handwriting can reveal their gender, where it starts to get fuzzy is that others claim they can tell much more including age, race, weight, and even mood. Going further down the rabbit hole, some employers have tried to use handwriting analysis to determine if an applicant is a match for a position. That seems a bit of a stretch to us.
Now, if you want to digitize a tiny bit of what makes you, you – then all you have to do it to fill out this (PDF) form and upload it to the interwebs. Out the other end will pop a true type font that you can save for yourself or share with the world. Why would you want to do that? This hack caught our eye as a way of adding annotations to our work in a more informal, yet still personal manner. Or maybe we just wanted to upload it to the cloud in hopes it would live forever. Either way, if you want to see some really amazing style, head on over to the “Penmanship Porn” subreddit where you can find some amazing chicken scratch.
This rig will take the letters you write on the touchpad using a stylus and turn them into digital characters. The system is very fast and displays near-perfect recognition. This is all thanks to a large data set that was gathered through machine learning.
The ATmega644 that powers the system just doesn’t have the speed and horsepower necessary to reliably recognize handwriting on its own. But provide it with a dataset to compare against and you’re in business. [Justin] and [Stephen] designed a neural network algorithm that took a large volume of character handwriting samples, and boiled them down into a set of correlations that can be referenced when encountering a new entry. This set is about 88 kilobytes, too much to store in the microprocessor, but easy to reference from an external flash memory device.
There’s plenty of gritty details in the write up linked above, but you may want to start with the video overview found after the break.