Shape Detection and Shape Removal - Revision history

Dgreenwood: /* What is Shape Removal? */

2025-08-08T19:55:32Z

What is Shape Removal?

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	== What is Shape Removal? ==		== What is Shape Removal? ==

			<blockquote>{{#lst:Glossary\|Shape Removal}}</blockquote>

	Shape Removal is an [[IP Command]] in Grooper that detects and removes regions of an image which match one or more trained image templates. This command is especially useful for cleaning up document images by erasing unwanted graphical marks—such as seals, logos, stamps, or watermarks—that may interfere with OCR, data extraction, or document presentation.		Shape Removal is an [[IP Command]] in Grooper that detects and removes regions of an image which match one or more trained image templates. This command is especially useful for cleaning up document images by erasing unwanted graphical marks—such as seals, logos, stamps, or watermarks—that may interfere with OCR, data extraction, or document presentation.

Dgreenwood: /* General configuration steps */

2025-08-08T19:49:42Z

General configuration steps

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	* Trigger conditional workflows in [[IP Profile]]s when a specific shape is present (using an IP Step or IP Group's "Next Step Expression" and/or "Should Execute Expression").		* Trigger conditional workflows in [[IP Profile]]s when a specific shape is present (using an IP Step or IP Group's "Next Step Expression" and/or "Should Execute Expression").
	* Provide visual overlays for reviewing detected shapes in diagnostics or the UI.		* Provide visual overlays for reviewing detected shapes in diagnostics or the UI.

	~~=== General configuration steps ===~~

	~~To use Shape Detection:~~

	=== General configuration steps ===		=== General configuration steps ===

Dgreenwood: /* Similarities and differences between Shape Detection and Shape Removal */

2025-08-08T19:48:43Z

Similarities and differences between Shape Detection and Shape Removal

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 * ''Both'' Shape Detection ''and'' Shape Removal detect shapes and store their information as layout data.
 * ''Only'' Shape Removal removes detected shapes from images, masking them out before further processing.
-** This is most useful for image cleanup prior to OCR or data extraction.
+** This is most useful for image cleanup prior to OCR.
 ** Shapes will be removed from an image permanently when Shape Removal is applied by the [[Image Processing]] activity.
 ** Shapes will be removed in-memory prior to OCR when Shape Removal is applied by the [[Recognize]] activity.
 === When to use each command ===
 * Use '''Shape Detection''' when you ''only'' need to extract shape information for separation, classification, or workflow triggers ''without'' altering the image.
-* Use '''Shape Removal''' when you want to eliminate shapes or to remove graphical noise prior to OCR or data extraction. Shape Removal will extract shape information as well.
+* Use '''Shape Removal''' when you want to redact shapes or to remove graphical noise prior to OCR. Shape Removal will extract shape information as well.
 == When are IP Profiles executed? ==

Dgreenwood: /* More on Proximity Measure */

2025-08-08T19:44:42Z

Dgreenwood: /* About Shape Detection settings */

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About Shape Detection settings

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 * Use '''Shape Detection''' when you ''only'' need to extract shape information for separation, classification, or workflow triggers ''without'' altering the image.
 * Use '''Shape Removal''' when you want to eliminate shapes or to remove graphical noise prior to OCR or data extraction. Shape Removal will extract shape information as well.
 == When are IP Profiles executed? ==

Dgreenwood: /* General configuration steps */

2025-08-08T19:44:07Z

General configuration steps

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	# The Shape Removal step will execute as part of the IP Profile's execution flow.		# The Shape Removal step will execute as part of the IP Profile's execution flow.
	#* See [[#When are IP Profiles executed?\|When are IP Profiles executed?]] for more information on different ways IP Profiles are used in Grooper.		#* See [[#When are IP Profiles executed?\|When are IP Profiles executed?]] for more information on different ways IP Profiles are used in Grooper.

			The detected shape information is stored as [[Layout Data\|layout data]], making it available for downstream separation, classification, or extraction.

	== Similarities and differences between Shape Detection and Shape Removal ==		== Similarities and differences between Shape Detection and Shape Removal ==

Dgreenwood: /* General configuration steps */

2025-08-08T19:43:54Z

General configuration steps

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	The detected shape information is stored as [[Layout Data\|layout data]], making it available for downstream separation, classification, or extraction.		The detected shape information is stored as [[Layout Data\|layout data]], making it available for downstream separation, classification, or extraction.

	==== More on Proximity ~~Meaure~~ ====		==== More on Proximity Measure ====

	The '''Proximity Measure''' property in the [[Shape Detection]] IP Command determines how Grooper compares a sample image (such as a stamp, seal, or logo) to regions of a document image when searching for matches. Each method has different strengths and is suited to different types of images or detection scenarios.		The '''Proximity Measure''' property in the [[Shape Detection]] IP Command determines how Grooper compares a sample image (such as a stamp, seal, or logo) to regions of a document image when searching for matches. Each method has different strengths and is suited to different types of images or detection scenarios.

Dgreenwood: /* General configuration steps */

2025-08-08T19:43:35Z

General configuration steps

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 To use Shape Removal:
 # Right-click an [[IP Profile]] or [[IP Group]] to add a Shape Removal [[IP Step]].
-# Select "Add Command" then "Feature Removal" then "Shape Removal".
+# Select "Add Command" then "Feature Detection" then "Shape Removal".
-# Configure the "Detection Settings" property with the same options as Shape Detection (sample images, confidence, angle, scale, etc.).
+# Add one or more sample images to the "Sample Images" property.
-# Adjust "Binarization" and "Dilation Factor" to control how shapes are detected and how much of the region is removed.
+#* Each sample should be a clear example of the shape you want to detect (such as a stamp, seal, or logo).
-# Test the IP Step/IP Profile using the "Tester" tab. Use diagnostic images and logs to review removal results and tune settings.
+#* To collect add a sample image, follow these steps:
 # The Shape Removal step will execute as part of the IP Profile's execution flow.
 == Similarities and differences between Shape Detection and Shape Removal ==

Dgreenwood: Created page with "'''Shape Detection''' and '''Shape Removal''' are two closely related IP Commands in Grooper. Both are designed to identify visual shapes—such as stamps, seals, logos, or other graphical marks—within document images. Shape Detection and Shape Removal save layout for the detected shape that can be used by the Read Zone and Detect Signature extractors or "Shape Detected" Separation Event. While Shape Detection locates and identifies shap..."

2025-08-08T19:28:38Z

Created page with "'''Shape Detection''' and '''Shape Removal''' are two closely related IP Commands in Grooper. Both are designed to identify visual shapes—such as stamps, seals, logos, or other graphical marks—within document images. Shape Detection and Shape Removal save layout for the detected shape that can be used by the Read Zone and Detect Signature extractors or "Shape Detected" Separation Event. While Shape Detection locates and identifies shap..."

New page

'''Shape Detection''' and '''Shape Removal''' are two closely related [[IP Command]]s in Grooper. Both are designed to identify visual shapes—such as stamps, seals, logos, or other graphical marks—within document images. Shape Detection and Shape Removal save [[Layout Data|layout]] for the detected shape that can be used by the [[Read Zone]] and [[Detect Signature]] extractors or "Shape Detected" [[Separation Event]]. While Shape Detection locates and identifies shapes, Shape Removal goes a step further by erasing detected shapes from the image.

== What is Shape Detection? ==

<blockquote>{{#lst:Glossary|Shape Detection}}</blockquote>

Shape Detection is an IP Command in Grooper that locates and identifies regions in a document image that match one or more provided sample images. This command is highly flexible and can detect a wide variety of shapes, including company logos, notary seals, stamps, and other graphical marks.

When the Shape Detection command is executed, it:

# Optionally preprocesses the image (binarization, resizing, dilation) to enhance shape visibility.
# Compares each sample image to the document image at multiple scales and angles, using a configurable proximity measure (such as Sum of Absolute Differences, Cross-Correlation, or Sum of Squared Distances).
# Reports regions that match the sample(s) above a configurable confidence threshold as detected shapes.
# Saves [[Layout Data|layout data]] describing the location, size, angle, and similarity score to sample images for each detected shape.

Shape Detection does not alter the original image. Its primary output is [[Layout Data|layout data]], which can be used by downstream processes such as document separation (via the "Shape Detected" [[Separation Event]]) or extraction (via the [[Read Zone]] and [[Detect Signature]] extractors).

=== Use cases for Shape Detection ===

Grooper can use the layout data generated by Shape Detection to:
* Separate documents based on the presence of stamps, seals, logos or other identifying marks (using the "Shape Detected" [[Separation Event]]).
* Classify documents based on the presence of stamps, seals, logos or other identifying marks (either during separation or through creative use of the the [[Read Zone]] or [[Detect Signature]] extractors).
* Using the "Shape Location", anchor [[Read Zone]] and [[Detect Signature]] extractors to shape locations on the page.
* Trigger conditional workflows in [[IP Profile]]s when a specific shape is present (using an IP Step or IP Group's "Next Step Expression" and/or "Should Execute Expression").
* Provide visual overlays for reviewing detected shapes in diagnostics or the UI.

=== General configuration steps ===

To use Shape Detection:

=== General configuration steps ===

To use Shape Detection:
# Right-click an [[IP Profile]] or [[IP Group]] to add a Shape Detection [[IP Step]].
# Select "Add Command" then "Feature Detection" then "Shape Detection".
# Add one or more sample images to the "Sample Images" property.
#* Each sample should be a clear example of the shape you want to detect (such as a stamp, seal, or logo).
#* To collect add a sample image, follow these steps:
#*# Select a Batch Page that has the sample you want to select. This can be done in any Grooper Document Viewer.
#*# Press the "Rubberband" button ({{RubberbandIcon}}) at the top of the Document Viewer.
#*# Drag a rectangle over the sample image you want to collect. Adjust the rectangle's position with the transform controls if needed.
#*# Press the "Copy" button ({{CopyIcon}}) at the top of the Document Viewer.
#*# Select the Shape Detection step in the IP Profile.
#*# Open the "Sample Images" editor (Press the "..." button).
#*# Press the "Paste" button ({{Template:PasteIcon}}).
#*# Repeat as needed to add multiple sample images.
#*# Enter a name for the sample image and press "OK".
# Using "Shape Name", enter a name for the shape. This will be saved to page's layout data when the shape is detected.
# Adjust detection settings as needed:
#* "Proximity Measure" – The algorithm used to compare the sample image(s) to the document image.
#* "Background Differencing" – Useful when shapes contain mostly whitespace. Enabling this prevents blank areas from producing high confidence scores.
#* "Minimum Confidence" – The threshold for reporting a detected shape.
#* "Maximum Angle" and "Angle Step" – Controls how much rotation is allowed when searching for shapes.
#* "Minimum Scale", "Maximum Scale", and "Scale Step" – Controls the size range and increment for shape detection.
# Adjust "Image Preprocessing" settings as needed:
#* These settings optimize shape visibility, improving Shape Detection's ability to detect shapes. Preprocessing adjustments only occur prior to detecting shapes and do not alter the final image.
#* "Processing Resolution" – The resolution at which detection occurs. Lower resolutions will process faster but may produce more false positives. Higher resolutions will be more accurate when processing small shapes and fine details by process slower.
#* "Binarization" – Controls how color and grayscale images are converted to black and white. Proper binarization is can improve shape matching for color and grayscale images.
#* "Dilation Factor" – Increasing this value will expand (dilate) the shape in the image to help connect faint or broken features.
#* "Region of Interest" – Restricts detection to a specific area of the image, if needed.
# While the default settings work well for most scenarios, you should test the IP Step/IP Group/IP Profile using the "Tester" tab. Use the Diagnostics panel's images and files to visualize results.
#* Ensure shapes are detected as you expect. Review the "Shape Locations" diagnostic. Detected shapes are highlighted and annotated with their confidence, scale, and angle.
#* Fine-tune the Shape Detection settings as needed.
# The Shape Detection step will execute as part of the IP Profile's execution flow.
#* See [[#When are IP Profiles executed?|When are IP Profiles executed?]] for more information on different ways IP Profiles are used in Grooper.

The detected shape information is stored as [[Layout Data|layout data]], making it available for downstream separation, classification, or extraction.

==== More on Proximity Meaure ====

The '''Proximity Measure''' property in the [[Shape Detection]] IP Command determines how Grooper compares a sample image (such as a stamp, seal, or logo) to regions of a document image when searching for matches. Each method has different strengths and is suited to different types of images or detection scenarios.

Below are the available Proximity Measures and their characteristics:

; '''SAD (Sum of Absolute Differences)'''
: This method computes the sum of the absolute differences between corresponding pixels in the sample and the document image region.
: * '''Strengths:''' High performance, simple to interpret, works well on clean, high-contrast images.
: * '''Weaknesses:''' Can be affected by lighting changes or background shading.
: * '''Recommended for:''' Most standard forms, stamps, and logos where lighting is consistent.

; '''CrossCorr (Normalized Cross-Correlation)'''
: This method computes the normalized cross-correlation between the sample and the document image region.
: * '''Strengths:''' Handles lighting and contrast variations better than SAD or SSD.
: * '''Weaknesses:''' Slightly slower than SAD, may be less effective if the sample and document have very different patterns.
: * '''Recommended for:''' Documents with variable backgrounds, shaded areas, or inconsistent lighting.

; '''SSD (Sum of Squared Distances)'''
: This method computes the sum of the squared differences between corresponding pixels in the sample and document image region.
: * '''Strengths:''' Can be useful when large mismatches should be strongly penalized.
: * '''Weaknesses:''' More sensitive to noise and outliers than SAD or CrossCorr.
: * '''Recommended for:''' Specialized cases where outlier differences are significant, but generally less robust for noisy or variable images.

'''Tip:'''
If you are not getting reliable matches with the default (SAD), try CrossCorr for documents with inconsistent lighting or backgrounds, or SSD for cases where you want to penalize large mismatches more heavily. Always review diagnostic output to evaluate the effect of each method and select the one that best matches your document types and image quality.

== What is Shape Removal? ==

Shape Removal is an [[IP Command]] in Grooper that detects and removes regions of an image which match one or more trained image templates. This command is especially useful for cleaning up document images by erasing unwanted graphical marks—such as seals, logos, stamps, or watermarks—that may interfere with OCR, data extraction, or document presentation.

When the Shape Removal command is executed, it:

# Analyzes the input image using the same detection logic as Shape Detection, based on the "Detection Settings" property.
# If a match is found, generates a mask for the detected region, optionally dilating the mask to ensure complete removal.
# Applies the mask to the image, erasing or suppressing the detected shape(s) using a mask-based dropout approach.
# Outputs the cleaned image with shapes removed, as well as [[Layout Data|layout data]] describing the detected shapes.

Shape Removal is most useful for cleaning up forms, removing stamps or logos before OCR, or eliminating watermarks that are not needed for downstream processing.

=== Use cases for Shape Removal ===

Shape Removal's use cases include all those of Shape Detection, plus:
* Cleaning up document images before OCR to prevent shapes from interfering with text recognition.
* Removing company logos, notary seals, or other marks that are not needed for downstream processing.
* Ensuring that only relevant content remains on the image for data extraction or export.

=== General configuration steps ===

To use Shape Removal:

# Right-click an [[IP Profile]] or [[IP Group]] to add a Shape Removal [[IP Step]].
# Select "Add Command" then "Feature Removal" then "Shape Removal".
# Configure the "Detection Settings" property with the same options as Shape Detection (sample images, confidence, angle, scale, etc.).
# Adjust "Binarization" and "Dilation Factor" to control how shapes are detected and how much of the region is removed.
# Test the IP Step/IP Profile using the "Tester" tab. Use diagnostic images and logs to review removal results and tune settings.
# The Shape Removal step will execute as part of the IP Profile's execution flow.

== Similarities and differences between Shape Detection and Shape Removal ==

=== Similarities ===

'''The big takeaway:''' Both Shape Detection and Shape Removal detect shapes and generate layout data.

* Both commands detect arbitrary shapes in document images using sample images and configurable detection logic.
* Both generate layout data describing the location, scale, angle, and confidence of detected shapes.
* Both can be configured with the same detection properties, including sample images, confidence threshold, angle, scale, binarization, and dilation.
* Both provide diagnostic output to assist with configuration and troubleshooting.
* Both are included as steps in an [[IP Profile]], allowing them to be combined with other image processing operations.

=== Differences ===

'''The big takeaway:''' Shape Detection does not alter the image. Shape Removal does alter the image.

* ''Both'' Shape Detection ''and'' Shape Removal detect shapes and store their information as layout data.
* ''Only'' Shape Removal removes detected shapes from images, masking them out before further processing.
** This is most useful for image cleanup prior to OCR or data extraction.
** Shapes will be removed from an image permanently when Shape Removal is applied by the [[Image Processing]] activity.
** Shapes will be removed in-memory prior to OCR when Shape Removal is applied by the [[Recognize]] activity.
* ''Only'' Shape Detection generates classification features for use in document separation, classification, or extraction logic (e.g., the [[Shape Detected]] event).

=== When to use each command ===

* Use '''Shape Detection''' when you ''only'' need to extract shape information for separation, classification, or workflow triggers ''without'' altering the image.
* Use '''Shape Removal''' when you want to eliminate shapes or to remove graphical noise prior to OCR or data extraction. Shape Removal will extract shape information as well.

== About Shape Detection settings ==

Shape Detection and Shape Removal have several configurable properties. In most cases, the default properties work well. However, each property allows you to fine-tune detection for your specific documents or graphical marks.

Below is a detailed explanation of each property, including its purpose, usage, and configuration guidance.

=== Detection settings properties ===

; '''Sample Images'''
: One or more images representing the shape(s) to be detected. Each sample should be a clear, representative example of the target shape.

; '''Shape Name'''
: The name assigned to detected shapes. Used in layout data, classification features, and diagnostics.

; '''Proximity Measure'''
: The algorithm used to compare the sample image to the document image. Options include Sum of Absolute Differences (SAD), Cross-Correlation, and Sum of Squared Distances (SSD).

; '''Minimum Confidence'''
: The threshold for reporting a detected shape. Only matches with a confidence score above this value are reported.

; '''Maximum Angle''' and '''Angle Step'''
: Controls how much rotation is allowed when searching for shapes, and the increment between angles tested.

; '''Minimum Scale''', '''Maximum Scale''', and '''Scale Step'''
: Controls the size range and increment for shape detection.

; '''Binarization'''
: Controls how the input image is converted to black and white for detection. Proper binarization is critical for accurate shape matching.

; '''Dilation Factor'''
: Expands the detected region before extraction or removal. Helps ensure the entire shape is included, especially if the template is smaller than the actual shape.

; '''Region of Interest'''
: Restricts detection to a specific area of the image. Useful for improving performance and reducing false positives.

=== Shape Removal properties ===

; '''Detection Settings'''
: Inherits all detection settings from Shape Detection.

; '''Binarization'''
: Controls how the image is binarized for mask generation.

; '''Dilation Factor'''
: Controls how much the detected region is expanded before removal.

=== Configuration tips ===

* Start with default values and review diagnostic images to assess detection and removal quality.
* Adjust properties incrementally, testing on multiple sample documents.
* Use diagnostic outputs ("Shape Mask", "Binarized", etc.) to visualize the effect of each property.
* Fine-tune for your specific document set, balancing thorough shape detection with preservation of important content.

== When are IP Profiles executed? ==

An '''IP Profile''' is executed whenever Grooper needs to process an image using a defined sequence or hierarchy of image processing operations. Execution typically occurs in the following scenarios:

* '''By the Image Processing activity:''' The Image Processing activity will apply the IP Profile and permanently alter the image.
* '''By an OCR Profile:''' OCR Profiles configured with an IP Profile will run the IP Profile on an image prior to handing it to the OCR engine. The image will not be permanently altered.
* '''By the Recognize activity's "Alternate IP" configuration:''' IP Profiles executed by this configuration will only execute feature detection commands (such as Shape Detection) to collect layout data.
* '''During a Review step:''' Users can manually execute an IP Profile from the Thumbnail Viewer (if configured to allow the user to do so).

Execution follows the order and logic defined in the IP Profile, including any conditional flow control or branching. Each step or group within the profile is applied in sequence, transforming the input image and producing results for each subsequent step.

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	The detected shape information is stored as [[Layout Data\|layout data]], making it available for downstream separation, classification, or extraction.		The detected shape information is stored as [[Layout Data\|layout data]], making it available for downstream separation, classification, or extraction.

	~~==== More on Proximity Measure ====~~

	The '''Proximity Measure''' property in the [[Shape Detection]] IP Command determines how Grooper compares a sample image (such as a stamp, seal, or logo) to regions of a document image when searching for matches. Each method has different strengths and is suited to different types of images or detection scenarios.

	~~Below are the available Proximity Measures and their characteristics:~~

	~~; '''SAD (Sum of Absolute Differences)'''~~
	~~: This method computes the sum of the absolute differences between corresponding pixels in the sample and the document image region.~~
	~~: * '''Strengths:''' High performance, simple to interpret, works well on clean, high-contrast images.~~
	~~: * '''Weaknesses:''' Can be affected by lighting changes or background shading.~~
	~~: * '''Recommended for:''' Most standard forms, stamps, and logos where lighting is consistent.~~

	~~; '''CrossCorr (Normalized Cross-Correlation)'''~~
	~~: This method computes the normalized cross-correlation between the sample and the document image region.~~
	~~: * '''Strengths:''' Handles lighting and contrast variations better than SAD or SSD.~~
	~~: * '''Weaknesses:''' Slightly slower than SAD, may be less effective if the sample and document have very different patterns.~~
	~~: * '''Recommended for:''' Documents with variable backgrounds, shaded areas, or inconsistent lighting.~~

	~~; '''SSD (Sum of Squared Distances)'''~~
	~~: This method computes the sum of the squared differences between corresponding pixels in the sample and document image region.~~
	~~: * '''Strengths:''' Can be useful when large mismatches should be strongly penalized.~~
	~~: * '''Weaknesses:''' More sensitive to noise and outliers than SAD or CrossCorr.~~
	~~: * '''Recommended for:''' Specialized cases where outlier differences are significant, but generally less robust for noisy or variable images.~~

	~~'''Tip:'''~~
	If you are not getting reliable matches with the default (SAD), try CrossCorr for documents with inconsistent lighting or backgrounds, or SSD for cases where you want to penalize large mismatches more heavily. Always review diagnostic output to evaluate the effect of each method and select the one that best matches your document types and image quality.

	== What is Shape Removal? ==		== What is Shape Removal? ==