Skills image-to-text

install

source · Clone the upstream repo

git clone https://github.com/TerminalSkills/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/TerminalSkills/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/image-to-text" ~/.claude/skills/terminalskills-skills-image-to-text && rm -rf "$T"

manifest: skills/image-to-text/SKILL.md

source content

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

The image is passed to Tesseract.js for optical character recognition
Tesseract segments the image into lines and words
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.