ocr-document-processor

To extract text from scanned PDFs and images, this Skill uses Optical Character Recognition (OCR) to process PNG, JPEG, and PDF files. It includes image preprocessing to enhance recognition accuracy before generating editable text output.

Yes, you can batch process multiple images for OCR text extraction. The Skill includes built-in batch processing capabilities to handle stacks of files like scanned receipts, converting them into structured outputs simultaneously.

OCR text extraction supports over 100 languages for global document processing. This extensive multi-language support allows you to accurately extract text from international documents and scanned images without needing separate configurations.

You can export extracted text to plain text, Markdown, JSON, or HTML formats. Additionally, the Skill facilitates structured table extraction directly to CSV format, enabling immediate data integration into spreadsheets or databases.

Image preprocessing improves OCR accuracy by optimizing the input visuals before recognition begins. By applying techniques via OpenCV and NumPy, it cleans up image noise and adjusts contrast, ensuring the OCR engine reads text more reliably.

For PDF OCR and image to text conversion, you need the pytesseract, Pillow, PyMuPDF, opencv-python, and numpy dependencies. These libraries handle image manipulation, PDF rendering, and the core OCR engine integration.