As data engineers, we’re always on the lookout for innovative solutions to extract clean structured data from unstructured sources like PDFs, images, and documents. That’s why I’m excited to share an update on DocStrange, an open-source structured data extraction library that’s just gotten even more powerful.
Previously, I introduced DocStrange as a way to extract data in Markdown, CSV, JSON, and other formats. Now, the library has taken a significant leap forward with the addition of a local web UI and an upgraded 7B model in cloud mode.
What’s New
The local web UI is a game-changer for developers who want to work with DocStrange without relying on command-line interfaces. It’s now easier than ever to extract structured data from your documents and images, with a user-friendly interface that streamlines the process.
But that’s not all. The upgraded 7B model in cloud mode means you can process even larger datasets with greater accuracy and speed. This is a significant improvement over the previous 3B model, and it opens up new possibilities for data extraction and analysis.
Why DocStrange Matters
DocStrange is more than just a data extraction tool. It’s a platform that helps you unlock the value of your unstructured data, turning it into actionable insights and business intelligence. With its open-source nature and flexible architecture, DocStrange is an ideal solution for developers, data scientists, and organizations looking to extract structured data at scale.
Get Started with DocStrange
If you’re interested in exploring DocStrange, head over to the GitHub repository to learn more. You can also check out the original post to see how DocStrange has evolved over time.
GitHub Repository: https://github.com/NanoNets/docstrange
Original Post: https://www.reddit.com/r/dataengineering/comments/1meupk9/docstrange_open_source_document_data_extractor/