Parse and extract text from scholarly documents
Parse documents to extract structured information
Extract and query terms from documents
Traditional OCR 1.0 on PDF/image files returning text/PDF
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Extract key entities from text queries
Search for similar text in documents
Search... using text for relevant documents
Analyze scanned documents to detect and label content
Extract information from documents by asking questions
Search documents for specific information using keywords
OCR that extract text from image of hindi and english
Search information in uploaded PDFs
Grobid End to End Evaluation is a tool designed to assess and validate the performance of Grobid, a machine learning model used for parsing and extracting text from scholarly documents. It evaluates the accuracy and reliability of Grobid's output by comparing it against ground truth data, ensuring the extracted text meets high standards of quality and usability.
What file formats does Grobid End to End Evaluation support?
Grobid End to End Evaluation supports PDF, XML, and plain text formats for both input documents and ground truth files.
Can I customize the evaluation metrics?
Yes, Grobid End to End Evaluation allows users to define custom metrics and weighting to suit specific requirements.
How do I handle documents with complex layouts?
Grobid is specifically designed to handle complex layouts, including multi-column text, tables, and figures. Ensure your ground truth accurately reflects these elements for proper evaluation.