A high-precision utility for converting images containing text, mathematical formulas, and diagrams into structured code formats.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install image-to-code
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install image-to-code using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Image-to-Code Transformer?
The image-to-code skill is a sophisticated automation tool designed to bridge the gap between static document screenshots and structured code output. By leveraging advanced OCR and Vision AI, this Openclaw Skills contribution automatically identifies document hierarchies, including headers, body text, and complex LaTeX-style formulas. It is particularly effective for developers and technical writers who need to digitize printed or captured documentation into standardized code templates for further processing.
This skill provides a robust framework for recognizing content types across images, ensuring that technical documents are not just transcribed, but intelligently converted into functional code snippets. Whether dealing with multi-level titles or intricate mathematical expressions, the tool maintains the original structural integrity of the source material while outputting it in a developer-friendly format.
Image-to-Code Transformer Use Cases
- Digitizing printed technical manuals and research papers into structured codebases.
- Converting complex mathematical equations from screenshots into LaTeX-compatible formula code.
- Automating the extraction of structured content from document snippets for CMS or database entry.
- Rapidly prototyping documentation structures by capturing and converting whiteboard sketches or printed outlines.
How Image-to-Code Transformer Works
- The skill initiates image preprocessing, including grayscale conversion, binarization, and denoising to optimize recognition quality.
- Deep learning models perform region segmentation to distinguish between text blocks, formula regions, and graphical images.
- Content recognition engines like PaddleOCR identify text while specialized tools like Pix2Tex or Vision AI handle mathematical expressions.
- The identified content is passed through a mapping logic that transforms raw data into specific code templates such as title1, body, or formula.
- The final output is assembled into a structured file, preserving the original top-to-bottom and left-to-right reading order.
Image-to-Code Transformer Setup
Prerequisites
Install the necessary OCR and image processing dependencies:
# Install core OCR and processing libraries
pip install paddlepaddle paddleocr pix2tex opencv-python pillow numpy
Usage
To convert a single image to code, run the following command:
python image_to_code.py input.png
For batch processing an entire directory, use the output flag:
python image_to_code.py *.png --output-dir ./output
Image-to-Code Transformer Data Schema & Taxonomy
| Content Type | Format Template | Mapping Rule |
|---|---|---|
| Level 1 Title | $word->title1("Title"); |
Strips prefixes like Chapter 1 or Part A |
| Level 2 Title | $word->title2("Title"); |
Strips numeric prefixes like 1.1 or (1) |
| Body Text | $word->body("正文=Content=".$F); |
Escapes quotes and wraps text in body function |
| Formula | $word->formula("LaTeX"); |
Converts math symbols to standard LaTeX syntax |
| Images | ![image] |
Identifies non-text graphical regions as image markers |
Image-to-Code Transformer Advanced Features
- Vision AI Integration: Support for high-accuracy formula recognition using GPT-4V or Claude Vision via Openclaw Skills.
- Intelligent Header Detection: Automatically classifies titles (H1, H2, H3) based on numbering patterns and font prominence.
- Contextual Error Correction: Post-processing logic to refine OCR results based on surrounding linguistic context.
- Batch Transformation: Ability to handle large-scale document digitization by processing entire folders of screenshots in a single execution.
SKILL.md
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