High error rates and significant manual rescanning can be acceptable in some applications, as long as there’s no better alternative.
It means that they make a lot fewer mistakes, but when they do, it can be subtle. For example, if the text is "the bat escaped by the window", a dumb OCR can write "dat" instead of "bat". When you read the resulting text, you notice it and using outside clues, recover the original word. An smart OCR will notice that "dat" isn't a word and can change it for "cat", and indeed "the cat escaped by the window" is a perfectly good sentence, unfortunately, it is wrong and confusing.
Considers, OCR was a very new field, such that a lot of the struggle was getting data into a place you could even try recognition against it. It should be no surprise that they were not able to succeed that often. It would be more surprising if they had a lot of different algorithms.
Yes, it may not make sense to use classical algorithms to try to recognize a cat in a photo.
But there are often virtual or synthetic images which are produced by other means or sensors for which classical algorithms are applicable and efficient.
even though I think Simon admits that most of it is obsolete after DL computer vision came about
I just don’t understand this. Why would new technology invalidate real understanding and useful computer algorithms?
The core of the approach was “find prominent horizontal lines, which exhibit symmetry about a vertical axis, and frame-to-frame consistency”.
Finding horizontal lines was done by computing variances in value. Finding symmetry about a vertical axis was relatively easy. Ultimately, a Kalman filter worked best for frame-to-frame tracking. (We processed video in around 120x90 output from variance algorithm, which ran on a PAL video stream.)
There’s probably more computing power on a $10 ESP32 now, but I really enjoyed the experience and challenge.
This was our vehicle: https://mercedes-benz-publicarchive.com/marsClassic/en/insta...
CNNs learn gabor filters. The AlexNet paper even shows this [0]
Or if you look at the work ViT built itself on, they show attention heads will also learn these fillers. [1] That's actually a big part of how ViTs work, the heads integrate this type of information
[0] https://papers.nips.cc/paper_files/paper/2012/hash/c399862d3...
FFT has this property that object orientation or location doesn't matter. As long as you have the signature of an object, you can recognize it anywhere!
What do you mean by that? Could you give me an example?
The FT is _NOT_ just a convolution, but under certain conditions a specific operation on FT terms is equivalent to a convolution.
alightsoul•8mo ago
Hydration9044•8mo ago