For my final project in 6.115, a microcomputer electronics class which I (and apparently nobody else) affectionately refer to as “leeblab”, I built a simple gestural input system. At its core lies an ordinary 8x8 LED matrix hacked into a low-res CCD and display coupled with a gutted expo dry erase marker used as a light pen. And per class requirements, it used a rube goldbergian cascade of TTL logic, an 8051, Cypress PSoC 5, an Arduino Pro Mini to process and massage the signals into USB HID compliant form, so that a computer might be able to use the contraption as a keyboard.
I had an 8x8 LED matrix laying around, and ‘twas the season that I had to come up with a final project for 6.115, a microcomputer electronics lab class. I vaguely recalled that an individual LED would generate a potential difference if you pummel it with enough photons. So I figured a cool and somewhat clever thing to do would be to create a display which could simultaneously act as a camera (pretty orweillian in retrospect).
I'm not totally sure about this, but I think this was the pinout of the LED matrix that I had. Notice that there doesn't seem to be any sensible mapping between spatial position and the corresponding pins
The LED matrix was something like this one. It’s wired using a technique called charlieplexing, where there’s a long wire along each row that connects the anodes of the LEDs, and another long weire along each column which connects the cathodes (modulo dyslexia).
You can imagine taking a battery and a few clips and touching one point along the row wires and another point on the column wires and see a single pixel light up at the intersection of those columns and rows.
On tablet or other touchscreen devices- of which there are quite a number of nowadays (as the New Year’s Eve post, I am obliged to include conjecture about the technological zeitgeist), a library such as Ocrad.js might be used to add handwriting input in a device and operating system agnostic manner. Oftentimes, capturing the strokes and sending them over to a server to process might entail unacceptably high latency. Maybe you’re working on an offline-capable note-taking app, or a browser extension which indexes all the doge memes that you stumble upon while prowling the dark corners of the internet.
If you’ve been following my trail of blog posts recently, you’d probably be able to tell that I’ve been scrambling to finish the program that I prototyped many months ago overnight at a Hackathon. The idea of the extension was kind of simple and also kind of magical: a browser extension that allowed users to highlight, copy, and paste text from any image as if it were plain text. Of course the implementation is a bit difficult and actually relies on the advent of a number of newfangled technologies.
If you try to search for some open source text recognition engine, the first thing that comes up is Tesseract. That isn’t a mistake, because it turns out that the competition is worlds away in terms of accuracy. It’s actually pretty sad that the state of the art hasn’t progressed substantially since the mid-nineties.
A month ago, I tried compiling Tesseract using Emscripten. Perhaps it was a bad thing to try first, but soon I learned that even if it did work out, it probably wouldn’t have been practical anyway. I had figured that all OCR engines had been powered by artificial neural networks, support vector machines, k-nearest-neighbors and their machine learning kin. It turns out that this is hardly the norm except in the realm of the actually-accurate, whose open source provinces live under the protection of Lord Tesseract.
GOCR and Ocrad are essentially the only other open source OCR engines (there’s technically also Cuneiform, but the source code is in a really really big zip file from some website in Russian and its also really slow according to benchmarks). And something I didn’t realize until I had peered into the source code is that they are powered by (presumably) painstakingly written rules for each and every detectable glyph and variation. This kind of blew my mind.
Anyway, I tried to compile GOCR first and was immediately struck by how easy and painless it had been. I was on a roll, and decided to do Ocrad as well. It wasn’t particularly hard- sure it was slightly more involved but still hardly anything.
If you know me in person, you’ll probably know that I’m not a terribly decisive person. Oftentimes, I’ll delay the decision until there isn’t a choice left for me to make. Anyway, serially-indecisive-me strikes again, so I alternated between the development of GOCR.js and Ocrad.js, leading up to a simultaneous release.
But in the back of my mind, I knew that eventually I would have to pick one for building my image highlighting project. I had been leaning toward Ocrad the whole time because it seemed to be a bit faster and more accurate when it came to handwriting.
Anyway, I spent a while building the demo page. It’s pretty simple but I wouldn’t describe it as ugly. There’s a canvas in the center which can be drawn to in arbitrary fonts pulled via the Google Font API. There’s a neat little thing which lets you draw things on the canvas. And to round out the experience, you can run the OCR engine on your own images, by loading the image file onto canvas to support any file the browser does (JPG, GIF, BMP, WebP, PNG, SVG) or by directly feeding the engine in its native NetPBM formats.
What consistently amazes me about Optical Character Recognition isn’t its astonishing quality or lack thereof. Rather, it’s how utterly unpredictable the results can be. Sometimes there’ll be some barely legible block of text that comes through absolutely pristine, and some other time there will be a perfectly clean input which outputs complete garbage. Maybe this is a testament to the sheer difficulty of computer vision or the incredible and under-appreciated abilities of the human visual cortex.
At one point, I was talking to someone and I distinctly remembered (I know, all the best stories start this way) a sense of surprise when the person indicated that he had heard of Tesseract, the open source OCR engine. I had appraised it as somewhat more obscure than it evidently was. Some time later, I confided about the incident with a friend, and he said something along the lines of “OCR is one of those fields that everyone comes across once”.
I guess I’ve kind of held onto that thought for a while now, and it certainly seems to have at least a grain of truth. Text embedded into the physical world is more or less our primary means we have for communication and expression. Technology is about building tools that augment human capacity and inevitably entails supplanting some human capability. Data input is a huge bottleneck, and while we’re kind of sidestepping the problem with things like QR codes by bringing the digital world into the physical. OCR is just one of those fundamental enabling technologies which ought to be as broad in scope as the set of humans who have interacted with a keyboard.
I can’t help but feel that the rather large set of people who have interacted with the problem character recognition have surveyed the available tools and reached the same conclusion as your miniature Magic 8 Ball desk ornament: “Try again later”. It doesn’t take long for one to discover an instance of perfectly crisp and legible type which results in line noise of such entropy that it’d give DUAL_EC_DRBG a run for its money. “No, there really isn’t any way for this to be the state of the art.” “Well, I guess if it is, then maybe it’ll improve in a few years- technology improves quickly, right?”
You would think that some analogue of Linus’s Law would hold true: “given enough eyeballs, all bugs are shallow”- especially if you’re dealing with literal eyeballs reading letters. But incidentally, the engine that absolutely everyone uses was developed three decades ago (It’s older than I am!), abandoned for a decade before being acquired and released to the world (by our favorite benevolent overlords, Google).
In fact, what’s absolutely stunning is the sheer universality of Tesseract. Just about everything which claims to have text recognition as a feature is backed by it. At one point, I was hoping that Mathematica had some clever routine using morphology and symbolic new kinds of sciences and evolved automata pattern recognition. Nope! Nestled deep within the gigabytes of code lies the Chuck Testa of textadermies: Tesseract.
It’s Halloween, and I still haven’t posted a monthly blog post and I don’t quite feel like retroactively posting something next month. I’m understandably quite starved for free time with my attempts to reconcile sleep with college with social interaction- and from the looks of it, I probably won’t be able to publish the blog post that I’ve been working on for the better half of this month before it the month ends.
For the past five days, I’ve started using an actual laptop- a late-2013 Macbook Pro Retina 15” (yes, I made it through the better part of two months of college without a laptop more sophisticated than a 2009-era Chromebook). Aside from the obligatory setup process and acclimation to the new operating system, and a mild bout of screen-size-anorexia (which with proper counseling, I’ve more or less recovered from- the 13” is actually somewhat small, but I still can’t quite shake the feeling that 15” is a smidgen too big), the process has been quite painless.
Getting a laptop slightly more capable than the Series 5 Chromebook (not the beefier Celeron model, the original Atom) is a quite long overdue change. I participated in my first hackathon (incidentally also the first time I’ve really written code since the start of the school year) during the beginning of the month. By the end of that 32-hour stretch, I did yearn for a functional trackpad, larger screen and more performant setup. But my shining knight in Unibody Aluminium armor would not have come until three weeks later. But I don’t think the productivity gains would have affected things too much- even with this dinky setup, the prototype scored the second place trophy.
The exact subject of the project was actually discussed briefly in the last progress update, on that long list of projects which I’ve yet to start. That night, I had actually taken the initiative to do a proper port of some Matlab implementation of the Stroke Width Transform. I hooked it up as a content script which would listen for mouse events over image elements and search for textual regions and draw semitransparent blue boxes where appropriate, and connected it to a node backend which would run tesseract to recognize characters. By the end of it all, I had enough for a pretty impressive demo.
Intermittently for more or less the entire month, I’ve been trying to improve the project- replacing some of the more hacky bits with more reliable implementations. I’ve read about some more algorithms, and experimented with different approaches to improve the method. I’m trying to add more stages to the text detection algorithm, such as the ability to segment an image into different lines, and improve the process of splitting lines into characters beyond mere connected components. But the process is rather tedious and with my limited free time, the project remains quite far away from public availability.
It must be infuriating to be in a situation where there’s a clear problem, and all the obvious remedies continually fail to produce any legitimate result. That’s what this blog is like: every month rolls by with some half baked ideas partially implemented and barely documented. And in the last day of the month, there’s a kind of panicked scramble to fulfill an entirely arbitrary self-enforced quota just to convince myself that I’m doing stuff.
Anyway, it’s pretty rare for me to be doing absolutely nothing, but mustering the effort to actually complete a project to an appreciable extent is pretty hard. This might be in some way indicative of a kind of shift in the type of projects that I try to work on- they’re generally somewhat larger in scope or otherwise more experimental. And while I may have been notorious before for not leaving projects at a well documented and completed state, these new ideas often languish much earlier in the development process.
There’s always pressure to present things only when they are complete and presentable, because after all timing is key and nothing can be more detrimental to an idea than ill-timed and poor execution. But at the same time, I think the point of this blog is to create a kind of virtual paper trail of an idea and how it evolves, regardless of whether or not it falters in its birth.
With that said, I’m going to create a somewhat brief list of projects and ideas that I’m currently experimenting with or simply have yet to publish a blog post for.
One of the earlier entries of the backlog is a HTML5 scramble with friends clone, acting highly performant on mobile with touch events and CSS animations while supporting keyboard based interaction on desktop. I’ve always been intending to build some kind of multiplayer real time server component so that people could compete in real time. Maybe at some point it’ll be included as a kind of mini game within protobowl.
The largest project is definitely Protobowl, which has just recently passed its one year anniversary of sorts. It’s rather odd that it hasn’t formally been given a post on this blog yet, but c’est la vie. Protobowl is hopefully on the verge of a rather large update which will add several oft-requested features, and maybe by then I’ll have completed something which is publishable as a blog post.
Font Interpolation/Typeface Gradients. I actually have no idea how long this has been on my todo list (years, no doubt), but the concept is actually rather nifty. With attributes like object size or color, things can be smoothly interpolated in the form of something like a gradient. The analogue for this kind of transition when applied to text would be the ability to type a word whose first letter is in one font, and the last letter being another font, with all the intermediate letters some kind of hybrid. I never did get quite far in successfully implementing it, so it may be a while until this sees the light of day.
I’ve always wanted to build a chrome extension with some amount of OCR or text detection capabilities so that people could select text which was embedded within an image as if it weren’t just an image. At one point I narrowed down the scope of this project so that the OCR part wasn’t even that important (the goal was then just some web worker threaded implementation of the stroke width transform algorithm and cleverly drawing some rotated boxes along with mouse movements). I haven’t had too much time to work on this so it hasn’t gone too far, but I do have a somewhat working prototype somewhere. This one too is several years old.
In the next few days, I plan on publishing a blog post I’ve been working on which is something of a humorous satire on some of the more controversial issues which have arisen this summer.
And there are several projects which have actually gotten blog posts which weren’t themselves formal announcements so much as a written version of my thinking process behind them. I haven’t actually finished the Pedant, in spite of the fact that the hardware is in theoretically something of a functional state (I remember that I built it with the intent that it could be cool to wear around during MIT’s CPW back in April, but classes start in a few days and it hasn’t progressed at all since). Probably one of the most promising ideas is the kind of improved, vectorized and modern approach to video lectures- but that too needs work.
I’m building the website for a local charity organization which was started by a childhood friend, and maybe I’ll publish a blog post if that ever gets deployed or something.