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Cover image for Case study: PDF Insights with AWS Textract and OpenAI integration
Łukasz Pluszczewski for Brainhub

Posted on • Updated on • Originally published at

Case study: PDF Insights with AWS Textract and OpenAI integration

Original problem - automated PDF summarization

The company approached us with the issue of a large quantity of data to sift through in the form of pitchdecks. While each pitchdeck is generally fairly short, in most cases around 10 slides each, the issue is the number of them to analyze. We were faced with the task of automating the extraction of the most important information from unstructured hard-to-parse format - PDF. Additionally, the data is in the form of slides: with a lot of graphical cues and geometric relations between words that convey information not easily inferred from the text itself. To make it easier to analyze a large amount of data, we would need a solution that would automate as much of that process as possible: from reading the document itself, to finding interesting pieces of information like names of people involved, financial data, and so on.

Why is text extraction so hard?

The first issue we faced was getting the text contents from a PDF file. While extracting text directly from PDF, using open source tools like pdf-parse (which is used internally by langchain’s pdf-loader) did the job most of the time, we still had some issues with it: some PDFs were not parsed correctly and the tool returned empty string (like in the case of Uber sample pitchdeck ), we’ve just got some words split into individual characters and so on.

Unfortunately, getting the text contents of the PDF was just the beginning. The text in PDF is all over the place: we had slides with two or three words, some tables, lists, or just paragraphs squished between images. Below is the example of text extracted from page 2 of the example reproduction of AirBnB early pitchdeck (link, extraction done with pdf-parse library):

Book rooms with locals, rather than hotels.
This is a PowerPoint reproduction of
an early AirBnB
via Business Insider @
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And this is one of the better ones!

While parsing text like this is hard in itself, we also would like to be able to modify what extract from the text. We may want to know what people are involved in a business. Or do we just want to get all financial data, or maybe just the name of the industry? Each type of data extracted requires a different approach to parsing and validating text, and then a lot of testing.

How can it be solved?

Reliable text extraction

First, we’ve decided to leave open-source solutions behind. We’ve used AWS Textract to parse PDF files. This way we don’t rely on the internal structure of the PDF to get text from it (or to get nothing - like in the case of the Uber example). Textract uses OCR and machine learning to get not only text but also spatial information from the document.

Here is the Textract result (with all geometric information stripped) from the same page of the AirBnB pitchdeck reproduction

Book rooms with locals, rather than hotels.
This is a PowerPoint reproduction of an early AirBnB pitchdeck via Business Insider @
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But that’s not all! Textract responds with a list of Blocks (like “Page”, or “Line” for a line of text), together with their position and relationships which we can use to understand the structure of the document better

    "BlockType": "LINE",
    "Confidence": 99.91034698486328,
    "Geometry": {
      "BoundingBox": {
        "Height": 0.22368884086608887,
        "Left": 0.8931880593299866,
        "Top": 0.024829095229506493,
        "Width": 0.05453843995928764
      "Polygon": [
          "X": 0.9477264881134033,
          "Y": 0.02518528886139393
          "X": 0.9472269415855408,
          "Y": 0.2485179454088211
          "X": 0.8931880593299866,
          "Y": 0.2481813281774521
          "X": 0.8936926126480103,
          "Y": 0.024829095229506493
    "Id": "7a88c32b-a0f6-4392-aed5-c5ab8977f162",
    "Page": 1,
    "Relationships": [
        "Ids": [
        "Type": "CHILD"
    "Text": "Welcome"
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Most of the time, we don’t need such details, so in our case, we use only a fraction of them.

Summarisation process and AI

Now to actually parse the text and pull what we want from it. For that, the only solution that seemed viable was to use a language model. While we tested some open-source solutions, they were not up to the task. Hallucinations were too common, and responses too unpredictable. Additionally, most capable Open Source models available today are not licensed to be used commercially. So we went with the OpenAI GPT-3.5 and GPT-4 models.

We’ve decided to first let the model summarise the text and include all information from the pitchdeck in that summary. That way we have text that is complete (not just the outline) and has a structure that is easier to work with. We’ve used the following prompt for each page of the document:

Below is the text extracted from a single page of pitchdeck PDF. Write a summary of the page. List all people, statistics, and other data mentioned.
Include only what is in the text, avoid adding your own opinions or assumptions.
Answer with the bullet point summary and nothing more.
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With additional instructions like “avoid adding your own opinions or assumptions” we minimize the hallucinations (models like to add fake data to the summary. GPT-3 even added a completely fake financial analysis!). When we have a summary of all pages we can ask the model to extract information from it. Here is an example of the prompt we’ve used to get the list of people referenced in the document:

List all people mentioned in the pages of the pitchdeck. Add their roles if that information is in the text.
Answer with the bullet point list and nothing more. Include only information that is in the pages, don\'t add your own opinions or assumptions.
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The summarisation returned by the models (both GPT3 and 4) is of good quality: the information returned is factual and whatever is plainly stated in the document will end up in the summary as well.

However, the extracting of the list of people is a different story. Models, especially GPT-3, often answer with a list similar to this (not an actual response):

- Uber
- John Doe (CEO)
- Anabella Moody (CPO/CTO)
- (123) 555-0123
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Not only this is clearly not a correct list of people, but also, the email was not in the source text at all, the model made it up!

We’ve also experimented with many variations of that prompt like:

  • Adding information that this is text extracted from PDF doesn’t seem to make any difference - models treat the input text the same way. When looking at the data there really isn’t any information for the model to infer anything from. We would need to include actual geometry data.
  • Skipping the summarisation part, and asking the model to get information from the text extracted from the whole document directly. This didn’t have much effect either (although I’ve seen a little worse responses at least in one case, but it was very subtle) which would suggest that we don’t need that summarisation step, especially when we do that for each page so we make quite a lot of requests. We’ve decided to keep it however as we may need a summary anyway.
  • Providing GPT with text together with spatial information returned by Textract. While this seems like a way to allow the model to infer some visual cues it is hard to figure out the right format. The JSON that Textract returns is quite verbose and it’s often too long to pass to the model (even with unnecessary fields stripped). Splitting up a page into smaller chunks seems wrong as the page context is often important to understand a chunk. This still needs investigating and more experiments.
  • While trying to solve the issue with inaccurate or hallucinated answers we’ve tried feeding the model with its answer so that it can validate and fix it. Unfortunately, our tests with GPT-3 failed - it didn’t see any issues with it’s made-up emails and phone numbers on a list that was supposed to contain the names of people. We need more tests with this approach using GPT-4 model though.

Next steps

What we miss and what is probably the most difficult is the ability to interpret the images and spatial relationships in PDF slides. While AWS Textract returns some spatial information it does not recognize images, and the data returned is hard to pass to the model. We’re still investigating how to make the model understand arrows, charts, and tables. Additionally, we would like to automate the process of online research e.g. find more information about companies mentioned in the documents using available APIs (like Crunchbase) or fetch more data on the people involved.


The case study addresses automating the extraction of vital details from numerous PDF pitchdecks. These decks are concise but numerous, making manual analysis impractical. The challenge involves extracting text and interpreting graphical elements. AWS Textract was employed for text extraction due to its OCR and layout understanding capabilities. OpenAI's GPT-3.5 and GPT-4 models were used to summarize and extract information, yet challenges arose in accurately extracting specific data like people's names or financial data. The study acknowledges the need to enhance image interpretation to understand visual elements better.

[EDIT]: Since the publication of this case study some new tools have appeared that make the process of parsing PDF presentations much, much easier. With multimodal language models like GPT-Vision, we can skip the OCR step and allow the system to interpret visual cues better than any pure text solution ever could. Stay tuned for more on that!

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