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Earlier quoted context omitted.
Cost, throughput, latency...
Traditional OCR is faster, cheaper, and much more reliable than LLMs
Class Act.
OCR has been solved long time ago with vision models. Solutions are consistent, reliable, and stable. What is the point of reinventing the wheel? I would definitely understand post processing, like extracting data, answering question .. etc, but why re-doing the OCR engine itself?
I guess, in theory, the prior distribution of language would allow for improved performance in some cases, especially where input quality is low.
Earlier quoted context omitted.
Cost, throughput, latency...
Traditional OCR is faster, cheaper, and much more reliable than LLMs
CJK have lots of character and high confusion rate.
Arabic scripts are complex and have lots of morphs.
Vietnamese have easily confused diacritics.
Thai have lots of non-standard fonts.
OCR has been solved long time ago with vision models. Solutions are consistent, reliable, and stable. What is the point of reinventing the wheel? I would definitely understand post processing, like extracting data, answering question .. etc, but why re-doing the OCR engine itself?
OCR has been solved long time ago with vision models. Solutions are consistent, reliable, and stable. What is the point of reinventing the wheel? I would definitely understand post processing, like extracting data, answering question .. etc, but why re-doing the OCR engine itself?
Detecting characters almost, layout no.
But if you are trying to ingest diverse documents with headings, multi-column layouts, headers and footers, ad space in the middle of your text, etc, vision-llms are a giant step forward. But you need the context of the previous page to make good decisions about the current page, which is where things quickly get janky (or slow, if you choose the naive approach)
Vision-llms also seem to deal much better with variance in scripts. Cursive, random Japanese in the middle of the text, weird math symbols, handwriting from three centuries ago, all "just works" without you even having to remember that this can happen
"We would like to thank Deepseek-OCR, Deepseek-OCR-2, PaddleOCR for their valuable models and ideas." Class Act.