[joakleaf]

I sort of agree... I do the same.

We also parse millions of PDFs per month in all kinds languages (both Western and Asian alphabets).

Getting the basics of PDF parsing to work is really not that complicated -- A few months work. And is an order of magnitude more efficient than generating an image in 300-600 DPI and doing OCR or Visual LLM.

But some of the challenges (which we have solved) are:

• Glyphs to unicode tables are often limited or incorrect • "Boxing" blocks of text into "paragraphs" can be tricky • Handling extra spaces and missing spaces between letters and words. Often PDFs do not include the spaces or they are incorrect so you need to identify gaps yourself. • Often graphic designers of magazines/newspapers will hide text behind e.g. a simple white rectangle, and place new version of the text above. So you need to keep track of z-order and ignore hidden text. • Common text can be embedded as vector paths -- Not just logos but we also see it with text. So you need a way to handle that. • Dropcap and similar "artistic" choices can be a bit painful

There are lot of other smaller issues -- but they are generally edge cases.

OCR handles some of these issues for you. But we found that OCR often misidentifies letters (all major OCR), and they are certainly not perfect with spaces either. So if you are going for quality, you can get better results if you parse the PDFs.

Visual Transformers are not good with accurate coordinates/boxing yet -- At least we haven't seen a good enough implementation of it yet. Even though it is getting better.

[reactordev]

We tried the xml structured route, only to end up with pea soup afterwards. Rasterizing and OCR was the only way to get standardized output.

[joakleaf]

I know OCR is easier to set up, but you lose a lot going that way.

We process several million pages from Newspapers and Magazines from all over the world with medium to very high complexity layouts.

We built the PDF parser on top of open source PDF libraries, and this gives many advantages: • We can accurately get headlines other text placed on top on images. OCR is generally hopeless with text placed on top of images or on complex backgrounds • Distinguish letters accurately (i.e. number 1, I, l, "o", "zero") • OCR will pick up ghost letters from images, where OCR program believes there is text, even if there isn't. We don't. • We have much higher accuracy than OCR because we don't depend on the OCR programs' ability to recognize the letters. • We can utilize font information and accurate color information, which helps us distinguish elements from each other. • We have accurate bounding box locations of each letter, word, line, and block (pts).

To do it, we completely abandon the PDF text-structure and only use the individual location of each letter. Then we combine letter positions to words, words to lines, and lines to text-blocks using a number of algorithms.

We use the structure blocks that we generated with machine learning afterwards, so this is just the first step in analyzing the page.

It may seem like a large undertaking, but it literally only took a few months to built this initially, and we have very rarely touched the code over the last 10 years. So it was a very good investment for us.

Obviously, you can achieve a lot of the same with OCR -- But you lose information, accuracy, and computational efficiency. And you depend on the OCR program you use. Best OCR programs are commercial and somewhat pricy at scale.

[user____name]

> To do it, we completely abandon the PDF text-structure and only use the individual location of each letter. Then we combine letter positions to words, words to lines, and lines to text-blocks using a number of algorithms. We use the structure blocks that we generated with machine learning afterwards, so this is just the first step in analyzing the page.

Do you happen to have any sources for learning more about the piecing together process? E.g. the overal process and the algorithms involved etc. It sounds like an interesting problem to solve.

[reactordev]

We were 99.99% accurate with our OCR method. It’s not just vanilla ocr but a couple of extractions of metadata (including the xml from the forms) and textract-like json of the document to perform ocr on the right parts.

A lot has changed in 10 years. This was for a major financial institution and it worked great.

[ramraj07]

Do you have your parser released as a service? Curious to test it out.