Computers haven’t done much for the quality of our already poor handwriting. However, a man paralyzed by an accident can now feed input into a computer by simply thinking about handwriting, thanks to work by Stanford University researchers. Compared to more cumbersome systems based on eye motion or breath, the handwriting technique enables entry at up to 90 characters a minute.
Currently, the feat requires a lab’s worth of equipment, but it could be made practical for everyday use with some additional work and — hopefully — less invasive sensors. In particular, the sensor used two microelectrode arrays in the precentral gyrus portion of the brain. When the subject thinks about writing, recognizable patterns appear in the collected data. The rest is just math and classification using a neural network.
If you want to try your hand at processing this kind of data and don’t have a set of electrodes to implant, you can download nearly eleven hours of data already recorded. The code is out there, too. What we’d really like to see is some easier way to grab the data to start with. That could be a real game-changer.
More traditional input methods using your mouth have been around for a long time. We’ve also looked at work that involves moving your head.
[Will Forfang] found a app that lets you take a picture of a math equation with a phone and ask for a solution. However, the app wouldn’t read handwritten equations, so [Will] decided to see how hard that would be, using a neural network.
The results are pretty impressive (you can also see the video below). [Will] used his own handwriting on a chalkboard and had the network train on that. He also went even further and added some heuristics to identify fraction bars and infer the grouping from the relative size of the bars.
Continue reading “Neural Network Does Your Homework”
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.
Continue reading “Ask Hackaday: DIY Handwriting Recognition”
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.
Continue reading “Machine Learning Lets Micro Decode Your Handwriting”