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 to eng ). 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 fra Output { "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.