Skip to main content

Text Exists

Uses OCR (Optical Character Recognition) to check if specific text exists on the screen or in a reference image.

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 or in the reference image.

Options​

  • Confidence - Confidence level for image recognition when using reference images (default: 0.95).

Outputs​

  • Result - Boolean value indicating whether the specified text was found (true/false).

How It Works​

The Text Exists node determines if specific text is present on the screen. When executed, the node:

  1. Takes a screenshot of the current screen or uses a reference image if provided
  2. Uses OCR (pytesseract) to extract text from either:
    • The entire screen
    • A specific region defined by anchor and target regions in a reference image
  3. Searches for the specified text within the recognized text
  4. Returns a boolean result indicating whether the text was found

Requirements​

  • Valid text to search for
  • Confidence value between 0 and 1 (when using reference images)
  • Tesseract OCR engine installed and configured

Error Handling​

The node will return specific errors in the following cases:

  • Empty search text - "Search 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"
  • Could not read image - "Could not read image"

Usage Notes​

  • This node is useful for conditional automation flows based on the presence of specific text
  • Can work with the entire screen or specific regions defined by reference images
  • When using regions, it first locates an anchor image and then searches for text in a target region
  • Works best with clear, high-contrast text
  • The confidence level affects the accuracy of anchor image recognition (when using regions)
  • Higher confidence values require more precise image matching
  • Lower confidence values may result in false positives but can handle variations better
  • Returns a simple boolean value that can be used in conditional logic
  • Useful for verifying that certain UI elements or messages are displayed