Also, their file size tends to be smaller than scanned image books. These ebooks were created from the original electronic layout files, and therefore are fully text searchable. We mark clearly which print titles come from scanned image books so that you can make an informed purchase decision about the quality of what you will receive. The text is fine for reading, but illustration work starts to run dark, pixellating and/or losing shades of grey. It's the problem of making a copy of a copy. Unfortunately, the resulting quality of these books is not as high. We essentially digitally re-master the book. Also, a few larger books may be resampled to fit into the system, and may not have this searchable text background.įor printed books, we have performed high-resolution scans of an original hardcopy of the book.
However, any text in a given book set on a graphical background or in handwritten fonts would most likely not be picked up by the OCR software, and is therefore not searchable. The result of this OCR process is placed invisibly behind the picture of each scanned page, to allow for text searching. Most older books are in scanned image format because original digital layout files never existed or were no longer available from the publisher.įor PDF download editions, each page has been run through Optical Character Recognition (OCR) software to attempt to decipher the printed text. Combine that with fixed-seed random number generators and you can probably get really good results.Īnother interesting idea would be to use Markov chains to actually learn how a dungeon looks like and then generate one using that Markov chain.Īnyway, the Procedural Content Generation Wiki is a good resource for that kind of stuff.These products were created by scanning an original printed edition. There are quite a few out there, for any kind of maze you can think of. To make the actual dungeon, you can use a maze generation algorithm. Drawback: The dungeons might look really, really weird. The question is then how to interpret the numbers. You will get the same chain of numbers for the same seed, e.g. You can ignore the noise part and do 'something' with math.random(). Noise functions use "random" functions that return the same value for a given set of coordinates.
The problem is that the noise generation is really, really slow. The most common is to use a noise function like perlin noise or value noise (implemented in my entry for the explosion contest) to create the terrain.
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