AI Agent Hub
Back to skills
🎨

Handwritten Text OCR

Design & Media Updated 2026.08.30

Paste the following prompt into your AI chat to install this skill:

Please install @user_628f4cb3/ocr-handwriting according to https://skillhub.cn/install/skillhub.md.

About this skill

Problem

Handwritten content often exists as images: lecture notes, contract signatures, paper forms, whiteboard photos. These pixels cannot be copied, searched, or edited directly until the text is extracted into structured output. Handwritten Text OCR takes such an image, detects text lines, and recognizes their content.

How It Works

The skill acts as a thin API client for an OCR backend rather than a local rules engine. The usual flow is:
- Check key: if scripts.config.settings.api_key is empty, ask for an API key and save it.
- Select tool: use scripts.tools.ocr_handwriting for an image URL or scripts.tools.ocr_handwriting_for_data_base64 for Base64 image data.
- Extract parameters: image links map to dataUrl; Base64 payloads map to dataBase64.
- Call and format: the tool returns raw, success, and message, then the raw result is cleaned and presented.

Scope and Caveats

It works best for legible handwritten line text, such as notes, signatures, and handwritten forms. Accuracy can drop with blur, severe skew, noisy backgrounds, or complex layouts. An API key is required, and image input must be either a URL or Base64 data before calling the tool.

Use Cases

  • Convert a scanned handwritten meeting-note image URL into searchable minutes text.
  • Extract handwritten signature content from contract scan pages for legal review and archiving.
  • Turn handwritten receipt notes or attachment comments into text for finance data entry.
  • Recognize handwritten answers in Base64 form screenshots submitted during product testing.

Best For

  • Operations assistants who need to turn handwritten note images into editable meeting summaries.
  • Legal specialists who need to extract handwritten signature content from contract scans.
  • Finance staff who need to convert handwritten receipt notes into text for data entry.
  • Test engineers who need to recognize handwritten form screenshots into structured field values.