The article provides a detailed overview of Python libraries for extracting tables from PDFs, covering both direct extraction tools (Camelot, PDFPlumber, etc.) and OCR solutions (Tesseract, EasyOCR, e
tc.) for scanned documents. Each tool is presented with features, code examples, and practical use cases to help developers choose the right solution for their specific needs.
Reasons to Read -- Learn:
how to select and implement the most appropriate PDF table extraction library from 10 different tools, each explained with specific use cases and code examples that you can immediately apply to your projects.
how to handle both structured PDFs and scanned documents using a combination of extraction libraries and OCR tools, with practical code snippets that demonstrate real-world implementation strategies.
advanced table extraction techniques using complementary tools like OpenCV and Pandas, including specific methods for detecting table boundaries and reconstructing rows and columns from raw extracted data.
5 min readauthor: Py-Core Python Programming
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