Dropout Method

Serves as an abstract base for defining settings and logic for dropping content out of an image using a mask in Grooper.

Remarks

The Dropout Method class provides the foundation for implementing mask-based dropout operations in Grooper image processing workflows. Dropout methods are used to remove or suppress specific content from an image by applying a mask, which defines the regions to be dropped out. This is commonly used for tasks such as removing form lines, backgrounds, or other unwanted features prior to further processing or data extraction.

Dropout Methods are highly configurable and can be extended to support a variety of dropout strategies. The core logic includes support for mask dilation and erosion, allowing you to fine-tune how aggressively the mask affects the image content. Derived classes implement the specific logic for applying the mask to the image.

Configuration and Usage

• Assign a Dropout Method to an image processing step or workflow where content needs to be selectively removed.
• Configure the 'Mask Dilation Factor' property to control the extent of mask expansion or contraction.
• Extend this class to implement custom dropout logic for specialized scenarios.

Best Practices

• Adjust the mask dilation factor to achieve the desired balance between removing unwanted content and preserving important features.
• Test dropout settings on representative images to ensure optimal results.
• Document the intent and configuration of each dropout method for maintainability and collaboration.

Properties

Name	Type		Description
Mask Dilation Factor	Int32	►	Controls the amount of dilation or erosion applied to the mask image. Default0 Min-16 Max16 The 'Mask Dilation Factor' property determines how much the mask is expanded (dilated) or contracted (eroded) before being applied to the image. This allows you to fine-tune the dropout effect, making it more or less aggressive depending on your requirements. Positive values will dilate the mask, causing the dropout region to expand and potentially remove more content from the image. Negative values will erode the mask, shrinking the dropout region and preserving more of the original content. A value of zero applies the mask as-is, with no dilation or erosion. Configuration Guidance Increase the value to more effectively drop out the edges of objects or to ensure complete removal of unwanted features. Decrease (use negative values) to avoid removing too much content, especially when important features are close to the dropout region. Typical values range from -16 to 16, but the optimal setting depends on the specific image and dropout scenario.

Derived Types

There are 2 implementations of Dropout Method.

Fill	Fills masked regions of an image with a solid color.
Inpaint	Digitally restores masked regions of an image by filling in missing or damaged areas.

Used By

OCR CleanupBox RemovalLine RemovalShape RemovalScratch RemovalBarcode RemovalBorder FillBlob RemovalSpeck RemovalHalftone RemovalHole Punch Removal