Imported from terminalskills/skills (skills/image-to-text/SKILL.md). Install upstream with npx skills add terminalskills/skills --skill image-to-text. Copyright stays with the author (MIT).

Image to Text

Overview

Extract all readable text from an image using OCR (Tesseract). Returns the full text content along with word-level bounding boxes and confidence scores.

• Reading text content from a screenshot or design mockup
• Extracting UI copy (labels, buttons, headings) so you don't have to retype it
• Getting text positions and bounding boxes from a design image

Instructions

1. The image is passed to Tesseract.js for optical character recognition
2. Tesseract segments the image into lines and words
3. Returns the full text plus word-level details (position, confidence)

Run the extraction script:

bash <skill-path>/scripts/image-to-text.sh <image-path> [language]

Arguments:

• image-path — Path to the image file (required)
• language — OCR language code (optional, defaults to eng). Common: eng, fra, deu, spa, chi_sim, jpn

The script outputs JSON with extracted text and metadata:

{
  "text": "Request work\nSuggestions\nPlumbing\nHVAC\nCleaning\nElectrical",
  "confidence": 87.4,
  "words": [
    {
      "text": "Request",
      "confidence": 94.2,
      "bbox": { "x0": 142, "y0": 180, "x1": 268, "y1": 204 }
    }
  ],
  "lines": [
    {
      "text": "Request work",
      "confidence": 95.1,
      "bbox": { "x0": 142, "y0": 180, "x1": 332, "y1": 204 }
    }
  ]
}

After extracting text, present the content grouped by lines and use the extracted text directly when implementing UI copy from a design.

Examples

Example 1: Extract text from a mobile app screenshot

bash <skill-path>/scripts/image-to-text.sh ./screenshot.png

Output:

Extracted text (87.4% confidence):

  Request work
  Suggestions
  Plumbing
  HVAC
  Cleaning
  Electrical

Found 6 lines, 6 words.

Example 2: Extract French text from a scanned invoice

bash <skill-path>/scripts/image-to-text.sh ./invoice-scan.png fra

Tesseract uses the French language model to correctly recognize accented characters and French-specific formatting. The extracted text can then be parsed for invoice fields like total, date, and line items.

Guidelines

• Tesseract works best with clean, high-contrast text. Screenshots of rendered UI work well. Photos of text at angles or with noise may produce poor results.
• Pass the correct language code as the second argument when processing non-English text. Tesseract needs the right language model to recognize characters.
• First run is slow because Tesseract downloads language data (~4MB for English). Subsequent runs are faster.
• For structured documents (tables, forms), post-process the extracted text to parse it into JSON or CSV format.