openocr-skills

What This Skill Does

The OpenOCR skill provides an advanced, lightweight, and highly efficient optical character recognition engine for OpenClaw. Built on a sophisticated 0.1B parameter model, this skill enables AI agents to read, interpret, and structure data from a wide variety of visual sources, including scanned documents, photographs of text, complex mathematical formulas, and tabular data. Unlike heavy, cloud-dependent OCR systems, OpenOCR is designed to run efficiently on local hardware while maintaining enterprise-grade accuracy. It supports specialized workflows ranging from simple text detection to full document layout analysis, converting unstructured image data into actionable text or structured Markdown formats. By integrating layout analysis with universal recognition, the skill acts as a bridge between physical media and digital intelligence.

Installation

To integrate OpenOCR into your agent's capability set, use the OpenClaw command-line interface. Run the following command in your terminal:

clawhub install openclaw/skills/skills/topdu/openocr-skill

Ensure that you have the necessary system dependencies and Python environment set up as outlined in the OpenClaw core documentation. Once installed, the skill will be automatically registered to your agent's registry.

Use Cases

• Digitization & Archiving: Convert legacy paper records, invoices, and physical receipts into searchable digital databases or spreadsheets.
• Academic & Research Assistance: Extract complex mathematical equations and technical formulas from screenshots or research papers to perform further symbolic computation.
• Document Structuring: Perform layout analysis on multi-column documents or forms, preserving the logical order of text and data tables.
• Data Extraction: Automate the entry of information from identity cards, labels, or product packaging into CRM or ERP systems.

Example Prompts

1. "Extract all text from this receipt image and format it as a markdown table with date, merchant, and total amount."
2. "Analyze this PDF page and perform layout analysis to extract the mathematical formulas and text content separately."
3. "Scan this photo of a handwritten note and convert it into a clean, digital text document."

Tips & Limitations

• Performance: While the model is optimized for personal PCs, high-resolution scans or extremely long documents may benefit from GPU acceleration; ensure your use_gpu flag is set to 'auto' for best results.
• Pre-processing: For blurry or low-light images, consider running basic image enhancement or cropping to the text region before calling the 'det' (detection) task to improve recognition accuracy.
• Complexity: The 'doc' task is highly powerful for layout analysis but requires more memory than the 'rec' (text recognition only) task. Use specific tasks rather than defaulting to complex ones when you only need simple text reading.