document-processing

To extract text and tables from scanned PDFs and images, you can use OCR and native PDF parsing to retrieve structured data. This process converts scanned documents like invoices into searchable text and extracts embedded tables for record management.

Yes, you can convert PDF documents to markdown, HTML, or plain text for editing and analysis. This format conversion aids workflows in data entry, archiving, and document analysis by structuring content from various document files.

Yes, batch processing is supported for document conversion and text extraction. You can instruct the system to process a batch of scanned PDFs to extract all tables and convert them into markdown format efficiently for automation purposes.

Yes, you need tesseract, pymupdf, and pandoc to extract structured data from PDFs. These dependencies are required to perform versatile processing, including OCR for scanned documents and native PDF parsing for data extraction.

The best way to digitize scanned invoices for record management is using OCR to convert them into searchable text and extracting embedded tables or emails. This streamlines workflows involving digitization, data extraction, and document conversion.

You should avoid using OCR for extracting text from PDF files when the documents are native PDFs with embedded text rather than scanned images, as native PDF parsing directly retrieves text and structured data more accurately without OCR.