In this episode, I’m joined by David Rosenberg, data scientist in the office of the CTO at financial publisher Bloomberg, to discuss his work on “Extracting Data from Tables and Charts in Natural Document Formats.”
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Bloomberg is dealing with tons of financial and company data in pdfs and other unstructured document formats on a daily basis. To make meaning from this information more efficiently, David and his team have implemented a deep learning pipeline for extracting data from the documents. In our conversation, we dig into the information extraction process, including how it was built, how they sourced their training data, why they used LaTeX as an intermediate representation and how and why they optimize on pixel-perfect accuracy. There’s a lot of interesting info in this show and I think you’re going to enjoy it.
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Mentioned in the Interview
- Tech at Bloomberg
- Bloomberg Tech Github
- Register for the AI Summit
- Check out @ShirinGlander’s Great TWIML Sketches!
- TWIML Presents: Series page
- TWIML Events Page
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“More On That Later” by Lee Rosevere licensed under CC By 4.0