image-ocr

What This Skill Does

The image-ocr skill for OpenClaw provides a robust interface for performing Optical Character Recognition (OCR) on various image file formats. By leveraging the industry-standard Tesseract engine, this skill allows users to transform visual data into machine-readable text seamlessly. Whether you are dealing with screenshots, scanned documents, or photographic text, this skill processes the pixels and returns clean, extractable string data. It supports a wide array of formats including PNG, JPEG, TIFF, and BMP, and offers language-specific support to ensure accurate character recognition across different character sets.

Installation

To utilize this skill, ensure you have the base Tesseract engine installed on your system. On Fedora or RHEL-based systems, execute 'sudo dnf install tesseract'. Once the dependency is met, install the OpenClaw skill by running: 'clawhub install openclaw/skills/skills/xejrax/image-ocr'.

Use Cases

This tool is invaluable for digitizing physical paperwork, automating data entry from screenshots, or extracting text from legacy image-based document archives. Developers and power users will find it highly effective for automating workflows where information is trapped in non-textual formats.

Example Prompts

1. "OpenClaw, extract the text from the screenshot named invoice_001.png and save it to my notes."
2. "Please run image-ocr on receipt.jpg and tell me the total amount listed at the bottom."
3. "Use the image-ocr skill to read the document.png file using the French language pack."

Tips & Limitations

For best results, ensure images have high contrast and clear typography. Low-resolution images or those with heavy noise/blur may result in lower accuracy. While Tesseract is powerful, it is not perfect for handwritten notes; it performs best with printed, standard fonts. If you are getting inconsistent results, try preprocessing your images with brightness or contrast adjustments before running the OCR command. Always specify the language flag if you are working with documents that contain non-English characters to significantly improve the character mapping accuracy.