asd - rishidaboo04/testing GitHub Wiki

| Tool | Pros | Cons | Limitations | Best Use Case | |------------------------------|----------------------------------------------------------------------|------------------------------------------------------------|------------------------------------------------------------|-----------------------------------------------------------------------------| | img2table | Specializes in extracting tables from images and scanned PDFs | May not handle highly complex tables as well as others | Best with scanned documents, not for standard PDFs | Scanned PDFs, images | | gmft==0.3.2 | Fine-tuned for table extraction in general documents | Not very well known, fewer resources available for support | Potential issues with non-standard tables | General table extraction from PDFs | | tabled-pdf==0.1.1 | Good for deep learning-based table extraction | Limited documentation and examples available | May struggle with complex or irregular table structures | PDF documents with structured tables | | marker==0.3.1 | Detects and marks table regions in PDFs | May require additional post-processing for accurate results | Not fine-tuned for direct extraction | Extracting table regions for further processing | | nougat | Extracts both text and tables effectively | May struggle with very intricate or nested tables | Requires deep learning resources | Mixed-content PDFs (text and tables) | | deepdoctection | Extracts both structured data (tables) and unstructured text | Can be complex to set up and configure | Focuses more on document detection than extraction quality | Extracting structured data alongside unstructured content | | open-parse (unitable) | Designed for parsing table structures from PDFs | May have trouble with highly irregular or sparse tables | Limited by how well the model understands specific tables | Parsing simple to moderately complex tables | | open-parse (tatr) | Uses TATR model for table extraction | Can struggle with non-tabular content | Best for documents where tables have clear separation | Extracting tables from documents with clear structure | | open-parse (pymupdf) | Combines table extraction with general PDF parsing using PyMuPDF | May not handle all table complexities well | Depends on PyMuPDF capabilities | PDFs with text and table extraction needs | | paddleocr | Supports OCR with table extraction, even from scanned PDFs | OCR may have lower accuracy on poor quality documents | Best used on OCR-readable documents | Scanned PDFs with tables | | alibaba/omniparser | General-purpose model for parsing structured documents, including tables | May require fine-tuning for specific document types | May not handle very complex or heavily formatted tables well | Parsing structured documents with clear layouts | | alibaba/DocXChain | Advanced model for extracting structured tables from PDFs | May not handle non-tabular content well | Best for structured table extraction | Extracting highly structured tables | | LayoutParser | Extracts both tables and text with deep learning | No recent updates, less support | May have compatibility issues with newer PDFs | PDFs with mixed content (text + tables) |