pdf

To extract text from scanned PDFs without a text layer, OCR workflows convert PDF pages to images using pdf2image and then apply pytesseract to extract readable text. This handles scanned-document digitization reliably when native text extraction fails.

Automating non-fillable PDF forms requires adding precise text annotations using accurate coordinate-based positioning and bounding box validation. This approach places text directly onto the document without relying on native AcroForm fields to generate completed outputs.

Yes, PDF table extraction is fully supported alongside standard text extraction. It pulls readable text and structured tables from documents simultaneously, providing structured data outputs ready for downstream analysis and writing workflows.

Handling PDF coordinate transformations requires accurately mapping image and document coordinate systems to position annotations correctly. The workflow validates bounding boxes and field IDs to ensure deterministic text placement on targeted document pages.

Yes, common PDF transformations like merging, splitting, and rotating pages are supported via Python and command-line tools. These batch page operations allow deterministic document manipulation across multiple files with reliable outputs.

Yes, ImageMagick and poppler-utils are required dependencies for PDF OCR workflows. They provide the underlying image conversion and rendering infrastructure needed by pdf2image to convert PDF pages into images for pytesseract text extraction.