PDF to Word (AI)

AI conversion uses machine learning to analyze document structure visually, detecting headings, paragraphs, lists, and tables even in scanned PDFs. Standard conversion relies on PDF metadata, which often fails for complex layouts or image-based documents.

The AI detects headings (multiple levels), paragraphs, bulleted and numbered lists, tables, figures, headers, footers, footnotes, and multi-column layouts. Each element is tagged with its type for accurate Word document generation.

Yes, AI conversion excels with scanned documents. The AI analyzes the visual layout and uses OCR (Optical Character Recognition) to extract text. This produces much better results than standard PDF parsing for image-based PDFs.

Yes. After AI analysis, you can review all detected elements, change their types (e.g., convert a paragraph to a heading), exclude unwanted sections, and fine-tune the structure before generating the Word document.

Detection accuracy typically exceeds 90% for well-formatted documents. The AI correctly identifies headings, body text, and lists in most cases. For unusual layouts, the interactive editor lets you make corrections before export.

The generated DOCX preserves heading hierarchy, paragraph styles, list formatting (bullets/numbers), table structure, and basic text styling. Font choices and exact spacing match the original PDF as closely as possible.

The AI detects multi-column layouts and can either preserve them as columns in Word or merge them into single-column text. You can choose the preferred handling in the structure editor.

Tables are detected separately using specialized table detection AI. Row and column boundaries are identified, and the content is extracted into proper Word tables with cell spanning preserved.