On 2022-01-23 12:47 p.m., Chuck Guzis via cctalk wrote:
On 1/23/22 10:16, Paul Koning via cctalk wrote:
Maybe. But OCR programs have had learning features for decades. I've spent quite a lot of time in
FineReader learning mode. Material produced on a moderate-quality typewriter, like the CDC 6600
wire lists on Bitsavers, can be handled tolerably well. Especially with post-processing that knows
what the text patterns should be and converts common misreadings to what they should be. But the
listings I mentioned before were entirely unmanageable even after a lot of "learning
mode" effort. An annoying wrinkle was that I wasn't dealing with greenbar but rather with
Dutch line printer paper that has every other line marked with 5 thin horizontal lines, almost like
music score paper. Faded printout with a worn ribbon on a substrate like that is a challenge even
for human eyeballs, and all the "machine learning" hype can't conceal the fact that no
machine can come anywhere close to a human for dealing with image recognition under tough
conditions.
The problem is that OCR needs to be 100% accuracy for many purposes.
Much short of that requires that the result be inspected by hand
line-by-line with the knowledge of what makes sense. Mistaking a
single fuzzy 8 for a 6 or a 3, for example can render code inoperative
with a very difficult to locate bug. Perhaps an AI might be programmed
to separate out the nonsense typos.
Old high-speed line printers weren't always wonderful with timing the
hammer strikes. I recall some nearly impossible to read Univac 1108
engineering documents, printed on a drum printer. Gave me headaches.
At least that's my take.
--Chuck
Document source is also a problem.
You would want to keep scan it at the best data format,
not something in a lossey format.
Ben.
