I've used abby finereader in the 0ast and for me it's the best ocr engine I've encountered. It has some other smart features as well in correcting missed words. I'm not sure if it's free though More on reddit.com

I've worked with document AI, and so far I think it has been best for the use case similar to what you've mentioned. However, I was able to get a lower cost using AWS textaract to extract text and then feed it to Gemini. I created a hybrid system to tackle costs. More on reddit.com

I don't know about supplying large texts to ChatGPT, but I do know about PDFs. PDFs come in many forms, some can be transformed into plain text very easily, others not so much. A PDF is a 100% dedicated to recreating an accurate representation of the document for some other medium, like a computer screen or a printer. It is litterally 0% made for extracting meaningful text or any other data from it. Basically three main types of PDFs can be distinguished, though technically every PDF can contain a mixture of these three types: A text and a font to render it in. Extracting the plain text from this is very easy, it contains the plain text. A text as a description of the shapes that together form the text. Extracting the textual meaning can be hard, depending on the font. A text as a bitmap image, like for instance from a scanned document. Extracting the textual meaning from this can be very hard. The limited resolution and font choice of the original text can pose considerable problems. Additionally a PDF can be expanded with "search text" metadata, this is basically a plain text version of the PDF and also puts it on par with category 1. Depending on the PDF documents you have rendering it into plain text can range from very easy to very hard. Category 2 and 3 documents basically need to be scanned with Optical Character Recognition (OCR) to make sense of them. Fortunately tools to assist in this also exist. For example muPDF's CLI tools can help you. muPDF also has logic to scan category 2 and 3 documents with Tesseract OCR. There's one specific type of data that's extremly hard to extract from a PDF, and that's tables. Tables make sense to a human mind, but their graphical rendering makes litterally 0 sense to a computer. Consequently extracting text from tables in a PDF is even more hard than extracting normal flowing text. More on reddit.com

After trying a dozen ChatGPT tools for documents, I recommend Sharly: (1) Many file-types beyond PDF with unlimited length (2) Auto-prompt transformation for best response quality (3) Sharing of conversation with co-workers/clients www.sharly.ai More on reddit.com