Image to Text
Extract all readable text from an image using OCR (Tesseract). Returns the full text content along with word-level bounding boxes and confidence scores.
When to Use
- 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
How It Works
- 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)
Usage
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 toeng). Common:eng,fra,deu,spa,chi_sim,jpn
Examples:
# Extract text from a screenshot
bash <skill-path>/scripts/image-to-text.sh ./screenshot.png
# Extract French text
bash <skill-path>/scripts/image-to-text.sh ./mockup.png fraOutput
{
"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 }
},
{
"text": "work",
"confidence": 96.1,
"bbox": { "x0": 274, "y0": 180, "x1": 332, "y1": 204 }
}
],
"lines": [
{
"text": "Request work",
"confidence": 95.1,
"bbox": { "x0": 142, "y0": 180, "x1": 332, "y1": 204 }
}
]
}| Field | Type | Description |
|---|---|---|
| text | String | Full extracted text, newline-separated |
| confidence | Number | Overall confidence score (0-100) |
| words | Array | Each word with text, confidence, and bounding box |
| lines | Array | Each line with text, confidence, and bounding box |
Present Results to User
After extracting text, present the content grouped by lines:
Extracted text (87.4% confidence):
Request work
Suggestions
Plumbing
HVAC
Cleaning
Electrical
Found 6 lines, 6 words.Use the extracted text directly when implementing UI copy from a design.
Troubleshooting
Low confidence / garbled text — 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.
Wrong language — Pass the correct language code as the second argument. Tesseract needs the right language model to recognize characters.
First run is slow — Tesseract downloads language data (~4MB for English) on the first run. Subsequent runs are faster.