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Greg
๐คย โขย 4m
i don't think there's any tool readily available for this. However i found this on gemini: See if it helps: You'll need a solution that can programmatically access your local files, extract text despite the unstructured nature (even without perfect OCR if the documents are already OCR'd but the data isn't fixed), and then process that text. Python with libraries like fitz (PyMuPDF) or pdfplumber for text extraction, and then natural language processing (NLP) libraries such as spaCy or NLTK for identifying relevant data, would be your best bet. Here's a conceptual outline: Iterate through files: Use Python's os module to list all PDFs in your specified directory. Extract text: For each PDF, use fitz or pdfplumber to extract the text content. Since you mention they are OCR'd PDFs, these libraries should be able to get the text. Information Extraction (NLP): Apply NLP techniques to identify key entities and clauses relevant to your "summary" and "relevant data." This is the most complex part, as it requires defining what "relevant data" means for your legal agreements (e.g., parties, dates, key clauses, terms). Summarization and Tabular Output: Develop logic to condense the extracted information into a summary for each document and then compile the "relevant data" into a pandas DataFrame, which can then be exported to a tabular format like CSV or Excel.
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