Speck Removal

The 'Max Speck Size' property sets the largest allowable width or height for a blob to be considered a speck. If a value is specified, only blobs where both the width and height are less than or equal to this value will be considered specks.

• Increase to remove larger specks or marks.
• Decrease to restrict removal to only the smallest artifacts.
• Set to an empty or zero value to ignore blob dimensions during speck analysis.

Specify the value in display units such as pt, in, or px. Review the Small Specks and Large Specks diagnostic images to tune this property.

The 'Max Speck Weight' property sets the largest allowable pixel count for a blob to be considered a speck. If a value is specified, only blobs with a pixel count less than or equal to this value will be considered specks.

• Increase to remove larger specks by area.
• Decrease to restrict removal to only the smallest artifacts.
• Set to an empty or zero value to ignore blob weight during speck analysis.

Specify the value in display units such as pt, in, or px. For example, 1pt will consider blobs with a pixel count less than or equal to that of a 1pt × 1pt square as specks. Review the Small Specks and Large Specks diagnostic images to tune this property.

The 'Quiet Zone Size' property specifies a border around character-sized objects where speck removal is suppressed. This helps prevent the accidental removal of punctuation or other small, meaningful marks near text.

• Set to an empty value to disable quiet zone enforcement.
• Increase to protect a larger area around text.
• Decrease to allow more aggressive speck removal near text.

The Quiet Zone Mask diagnostic image shows the bounds of the quiet zone. Use this property to balance speck removal with preservation of important content.

The 'Quiet Zone Override' property allows very small specks to be removed even if they fall within a quiet zone. This ensures that the smallest artifacts are always removed, regardless of their proximity to text.

• Increase to allow removal of larger specks within the quiet zone.
• Decrease to restrict override to only the tiniest artifacts.

Specify the value in display units such as px, pt, or in. Review the Small Specks diagnostic image to tune this property.

Can be one of the following values:

The 'Connectivity' property determines how pixels are grouped into blobs for speck detection. Choose between 4-connectivity (pixels connected horizontally and vertically) or 8-connectivity (pixels connected in all directions).

• Use 4-connectivity for stricter grouping, which may result in more, smaller blobs.
• Use 8-connectivity to group diagonally connected pixels, which may result in fewer, larger blobs.

The choice of connectivity can affect which artifacts are detected as specks. Review the Binarized and Specks diagnostic images to tune this property.

Can be one of the following types:

The 'Dropout Method' property determines the technique used to remove or replace regions of the image identified by the feature mask. This allows you to control the visual result of the dropout operation, depending on your processing goals.

Common dropout methods include:

• Filling masked regions with a solid color (such as white or the detected background color).
• Replacing masked regions with interpolated content from surrounding pixels.
• Applying custom fill logic as defined by the selected Dropout Method.

Choosing the appropriate dropout method is important for maintaining the readability and appearance of the processed image. For example, filling with white may be suitable for removing background noise, while interpolation may be preferred when removing lines or halftones from text regions.

Example: To remove halftone patterns and fill the resulting regions with the detected background color, select the 'Background Fill' option from the 'Dropout Method' dropdown.

Review the diagnostic images generated in diagnostic mode to verify that the selected method produces the desired effect on your sample images.

The 'Binarization Settings' property determines the thresholding method and parameters used to create the binarized image that serves as the basis for feature detection in Binary Dropout commands.

Binarization is a critical preprocessing step: it transforms the input image into an 8-bit black and white version, making features such as barcodes, halftones, or lines more distinct and easier to detect.

Configuration Guidance

• Select the thresholding method using the 'Method' property within 'Binarization Settings'.
• Adjust additional parameters such as threshold value, window size, kernel type, and adjustment as needed for your document type.
• For most standard documents, 'Auto' or 'Adaptive' methods provide the best results.
• For documents with variable backgrounds or lighting, use 'Adaptive' or 'Dynamic' and tune the window size and kernel type.

Notes

• The quality of the binarized image directly affects the accuracy of feature detection and dropout.
• Review the diagnostic output to ensure that the features you wish to remove are clearly visible in the binarized image.

The 'Command Type Name' property shows the user-friendly name of the IP Command as it appears in the Grooper UI. This helps users identify which command is being configured or executed, especially when working with IP Profiles containing multiple commands.

This property is read-only and is automatically set based on the command's type.

The 'Supported Pixel Formats' property lists the image pixel formats that the IP Command can process. If an image does not match one of these formats, the command will be skipped for that image.

• By default, all pixel formats are supported unless a derived command restricts this list.
• Example formats include Pixel8bppGrayscale, Pixel24bppBgr, and Pixel1bppIndexed.

Use this property to verify compatibility when building IP Profiles or troubleshooting why a command did not execute.