Extracting data from PDFs efficiently has always been a nightmare for developers and businesses alike. Among the many elements trapped inside a PDF, tables are the most stubborn — with merged cells, missing borders, misaligned text, and even scanned artifacts.
Traditional tools like Tabula, Camelot, and rule-based parsing work fine for simple documents, but once you encounter real-world PDFs — think financial statements, lab reports, or scanned invoices — they often fail to deliver accurate results.
Interestingly, just a few months ago I wrote about Docling, another powerful open-source library for parsing and converting PDFs into structured formats. Docling was a significant step forward because it handled layout-aware extraction and made it easier to transform documents into clean text and tables.
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But what we’re seeing now with Document-Pretrained Transformers (DPT) pushes the bar even higher — bringing near plug-and-play table extraction, with far greater accuracy and robustness against messy, irregular documents.
