Dealing with non-standard formatting in legal PDFs requires a combination of robust text extraction, structure analysis, and post-processing. Legal documents often contain scanned pages, inconsistent layouts, handwritten notes, or embedded tables that break standard parsing tools. The first step is to use libraries like PyPDF2, pdfplumber, or Tesseract OCR (for scanned text) to extract raw content. For example, a PDF might mix text layers with images, requiring OCR to process scanned sections while using traditional methods for digital text. Developers must also handle text positioning—legal clauses might be split across columns or pages, requiring coordinate-based analysis to reconstruct the correct order.
Next, analyze the document’s structure to identify headers, footers, section markers, and other recurring elements. Legal documents often use non-standard section numbering (e.g., "§ 1.2(a)(iii)") or place critical terms in margins. Regular expressions can help detect patterns like “Article X” or “CLAUSE 5,” but layout-based tools like PDFMiner or Apache PDFBox may be needed to map text coordinates. For tables or forms, tools like Camelot or Tabula can extract structured data, but custom logic might be required to handle merged cells or inconsistent formatting. For example, a lease agreement might list obligations in a table with varying row heights, requiring iterative checks to align data correctly.
Finally, validate and normalize the extracted data. Legal texts often reference other sections or appendices, so cross-checking extracted content against known templates or schemas ensures completeness. Tools like spaCy or custom rule-based systems can flag anomalies—e.g., missing signature blocks or mismatched clause references. For instance, a parsed contract might miss a subsection due to a page break, requiring logic to stitch split sections using context (e.g., “continued on page 12”). Post-processing steps like removing duplicate headers/footers (identified by repeated text or fixed coordinates) and standardizing date formats (e.g., converting “12th March 2023” to “2023-03-12”) improve usability. Testing with diverse samples—such as affidavits, patents, or court rulings—helps refine the pipeline for edge cases.