Effectively every text document, especially those written in languages with small alphabets like English or German, consists of many repeated letters (also known as glyphs) on each page. JBIG2 tries to segment each page into glyphs then uses simple pattern matching to match up glyphs which look the same:
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JBIG2 doesn’t actually know anything about glyphs and it isn’t doing OCR (optical character recognition.) A JBIG encoder is just looking for connected regions of pixels and grouping similar looking regions together. The compression algorithm is to simply substitute all sufficiently-similar looking regions with a copy of just one of them:
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In this case the output is perfectly readable but the amount of information to be stored is significantly reduced. Rather than needing to store all the original pixel information for the whole page you only need a compressed version of the “reference glyph” for each character and the relative coordinates of all the places where copies should be made. The decompression algorithm then treats the output page like a canvas and “draws” the exact same glyph at all the stored locations.
There’s a significant issue with such a scheme: it’s far too easy for a poor encoder to accidentally swap similar looking characters, and this can happen with interesting consequences. D. Kriesel’s blog has some motivating examples where PDFs of scanned invoices have different figures or PDFs of scanned construction drawings end up with incorrect measurements. These aren’t the issues we’re looking at, but they are one significant reason why JBIG2 is not a common compression format anymore.
https://googleprojectzero.blogspot.com/2021/12/a-deep-dive-into-nso-zero-click.html?m=1