ocr-and-documents

You can extract text from scanned PDFs and images using OCR by applying optical character recognition to recognize text within image-based documents. This skill automates that process, providing accurate text extraction from scanned receipts and documents.

The best way to perform PDF editing and update text like titles and dates is by using a document processing library that supports direct content modification. This skill enables basic PDF content editing, allowing you to correct typos and update meeting notes efficiently.

Yes, you can use pymupdf and marker-pdf for document processing and reading order detection. These Python libraries are utilized by this skill to handle text-based PDFs, extract tables and equations, and accurately detect the reading order of document elements.

Yes, this OCR approach works with complex document elements like tables, equations, and forms. The document processing capabilities include features for recognizing and extracting these specific structures from scanned documents and PDFs, enhanced further by LLM-based accuracy improvements.

To extract images and remove headers and footers from PDF documents, you apply a document processing script that isolates and removes these specific page elements. This skill includes features for headers and footers removal alongside image extraction from your PDFs.