We’ll come back to our trilobite friend when we start searching through our dataset. Our development environmentĪs usual we’ll use a Google Colab notebook.Ī rendering of a trilobite. Any more than that and we’d be here forever. We’re going to focus on just extracting text, images and tables. Or even if you just want to remix your paleontology textbook collection. Therefore being able to process and extract this data is vital for business intelligence. But if you can’t extract anything you’re just left with big blobs of almost meaningless data. If you can extract information, you can do all sorts of things, like searching through it, summarizing it, collating it, and so on. Extracting even a subset of that information can be a lot of work. PDF is a gnarly format - one PDF file can contain text, images, tables, audio, video, 3D meshes, and lots of other ridiculous things. ![]() But we’ll go over the high-level stuff to give you a good big-picture understanding. We won’t go through every little bit of code here - check the notebook for that. Filter results by type (text, image, table). ![]() Let you index and search through PDF text, tables and images, using text or image as search term.So, in this blog post and notebook, we’re going to look at how to do just that. As if I haven’t suffered enough with PDFs… Since then I’ve heard from a lot of people that they want to go further, and search images and tables too. Product.page_number=6 product.text()='Natural Dates, 500g\nHeba / Sky Light / Sapphire' price.text()='9895\n120.In previous blog posts ( 1, 2, 3) and notebooks, we built a basic PDF search engine. Product.page_number=6 product.text()='Laitue Butterhead, \nField Good' price.text()='2495\n35.00' Product.page_number=6 product.text()='Tomato Salad / Italian Plum, 1kg\nEsprit Vert' price.text()='11995\n165.00' Price = prices.vertically_in_line_with(product).above(product) "in line" - we can modify the x0,x1 coords directly to use a larger ![]() The "in line" filters have a capped tolerance which is too smallįor some products in this catalog as the price is not always directly This means you have to bring in more complicated OCR or ML approaches that are far from 99 or 100% accurate.įeel free to PM me if you have any more questions!Įach price is "above" the description and nearly always "aligned" in a "column" from py_pdf_parser.loaders import load_file This is because once you start to work with a wide variety PDFs that aren’t as straight forward as just text in a document, you introduce a scholastic element to the problem. Unfortunately, there is no one Python module that is going to extract PDF text 100% of the time correctly. I’ve spent a long time going over open source solutions to this and the best two I’d say are Excalibur and Apache Tika. While I unfortunately cannot share the code I used to extract this text, I will tell you that for what I think your doing, the best solution will require a few things. It is especially tricky once you get a wide variety of PDFs (including PDFs with image based text or tables). Hey, I’ve spent quite a bit of time looking at extracting text as accurately as possibly from PDFs, it’s turns out that it is not as simple as it might seem.
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