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Find Text

Uses OCR (Optical Character Recognition) to locate specific text on the screen and returns its position information.

Common Properties​

  • Name - The custom name of the node.
  • Color - The custom color of the node.
  • Delay Before (sec) - Waits in seconds before executing the node.
  • Delay After (sec) - Waits in seconds after executing node.
  • Continue On Error - Automation will continue regardless of any error. The default value is false.

Inputs​

  • Search Text - The text to search for on the screen.

Options​

  • Confidence - Minimum confidence level for text recognition (default: 0.95).

Outputs​

  • Result - Position information of the found text including coordinates and dimensions.

How It Works​

The Find Text node uses OCR to locate specific text on the screen. When executed, the node:

  1. Takes a screenshot of the current screen or uses a reference image if provided
  2. Processes the image using OCR (pytesseract) to extract text
  3. Searches for the specified text within the recognized text
  4. Returns position information for all instances of the found text

Requirements​

  • Valid text to search for
  • Confidence value between 0 and 1
  • Tesseract OCR engine installed and configured
  • Reference image (optional, for targeted search areas)

Error Handling​

The node will return specific errors in the following cases:

  • Empty text - "Text can not be empty"
  • Missing confidence - "Confidence can not be empty"
  • Invalid confidence format - "Confidence is not valid"
  • Confidence out of range - "Confidence must be between 0 and 1"
  • Text not found - "Could not find specified text"
  • Could not read image - "Could not read image"

Usage Notes​

  • The confidence level affects the accuracy of text recognition
  • Higher confidence values require clearer, more readable text
  • Lower confidence values may recognize more text but with less accuracy
  • Works best with clear, high-contrast text
  • Can be used with a reference image to limit the search area
  • Supports multi-word text searches
  • Returns detailed position information for each text instance found
  • Useful for automating interactions with text-based UI elements
  • May require adjusting confidence levels based on text quality and font