Auto Deskew

This property defines the largest angle (in either direction) that the skew detection algorithm will consider when searching for skew. For example, a value of 5.0 means angles from -5.0° to +5.0° will be tested. Increasing this value allows correction of more severely skewed images, but also increases processing time because more angles must be evaluated. Set this value based on the maximum expected skew in your documents.

• Performance Impact:
  Higher values increase the number of angles tested, which can significantly increase processing time, especially when combined with a small 'Angle Precision'.
• Typical Values:
  For most business documents, a value between 3 and 7 is typical. For highly skewed or camera-captured images, consider increasing this value.
• Example:
  If set to 10 and 'Angle Precision' is 0.5, the algorithm will test 41 angles: -10.0°, -9.5°, ..., 0.0°, ..., 9.5°, 10.0°.

Can be one of the following types:

This property controls the increment between each angle tested by the skew detection algorithm. Smaller values (such as 0.25 for 1/4°) result in finer precision and potentially more accurate deskewing, but increase processing time. Larger values (such as 1.0) speed up processing but may result in less precise correction.

• Performance Impact:
  Lower values (higher precision) increase the number of angles tested, which can slow down processing, especially on high-resolution or complex images.
• Accuracy:
  Finer precision allows the algorithm to detect and correct small skews more accurately, which is important for OCR and data extraction.
• Example:
  With 'Maximum Angle' set to 5 and 'Angle Precision' set to 0.25, the algorithm will test 41 angles: -5.00°, -4.75°, ..., 0.00°, ..., 4.75°, 5.00°.
• Tuning Tip:
  Start with 0.25 for most documents. Increase for speed, decrease for accuracy.

This property determines which blobs in the image are considered as potential text characters for skew detection. Only blobs whose size falls within this range are used to estimate the skew angle.

• Purpose:
  Excludes non-text elements (such as specks, punctuation, graphics, or lines) and focuses the analysis on actual text.
• Tuning:
  Use the 'Characters' diagnostic image to verify that the selected range includes most text and excludes noise. Adjust the minimum and maximum values to match the expected font sizes in your documents.
• Example:
  For standard business documents, a range of 3pt to 20pt is typical. For documents with very small or large text, adjust accordingly.
• Impact:
  If the range is too narrow, not enough blobs may be detected, reducing accuracy. If too wide, noise may be included, leading to incorrect skew detection.

This property limits the number of detected character blobs used in the skew analysis. The algorithm selects blobs from both the beginning and end of the document to ensure representative sampling.

• Performance vs. Accuracy:
  Lower values increase speed but may reduce accuracy, especially on dense or long documents. Higher values improve accuracy but increase processing time.
• Sampling:
  If set to 1000, the first 500 and last 500 blobs are used. This helps balance analysis across the page.
• Tuning:
  Use the 'Characters' diagnostic image to see which blobs are included. Increase this value for documents with many lines of text, decrease for short forms or tickets.
• Impact:
  Too low may miss important text, too high may slow down processing.

If the detected skew angle does not meet or exceed this confidence threshold (a value between 0 and 1.0), the image will not be rotated and the skew angle will be reported as 0°.

• Purpose:
  Prevents over-correction when the algorithm is uncertain about the skew angle, which can happen on blank, noisy, or low-text images.
• Tuning:
  Use diagnostic logs to review the confidence of detected angles. Lower values allow correction even when the algorithm is less certain, while higher values require greater certainty before applying a correction.
• Example:
  A value of 0.25 is a reasonable starting point for most documents. Increase if you see false corrections, decrease if some skewed images are not being corrected.

The binarization process converts the input image to black and white, which is essential for reliable character blob detection.

• Purpose:
  Ensures that text stands out clearly from the background, making it easier to detect character blobs.
• Tuning:
  Adjust the method and parameters to produce an image where each character forms a distinct blob. Use the 'Binarized' diagnostic image to verify the effect of your settings.
• Methods:
  For most documents, the 'Auto' or 'Adaptive' binarization methods provide good results. For documents with uneven backgrounds or colored paper, experiment with different methods and settings.
• Impact:
  Poor binarization can cause missed or merged blobs, reducing skew detection accuracy.

Can be one of the following values:

This property controls the quality of the rotated (deskewed) image. Higher-quality modes such as Cubic produce smoother results but may be slower. Lower-quality modes such as NearestNeighbor are faster but may introduce artifacts.

• Quality vs. Speed:
  Cubic is recommended for a balance of quality and speed. Use NearestNeighbor for speed-critical scenarios or when working with binary images.
• Artifacts:
  Lower-quality modes may introduce jagged edges or blurring, especially on text.
• Tuning:
  Review output images to ensure text remains sharp and readable after deskewing.

Can be one of the following types:

When the image is rotated, triangular regions at the corners may be exposed. This property specifies the color used to fill those areas.

• Default Behavior:
  If no color is specified, the dominant background color of the image is used automatically.
• Use Cases:
  Set this property if you require a specific background color for your output images, such as pure white for forms or a corporate color for branding.
• Tuning:
  For best OCR results, use a color that matches the document background.

When enabled, the output image will be resized as needed to ensure that no part of the rotated image is lost. When disabled, parts of the image near the corners may be cropped after rotation.

• Use Cases:
  Enable this property if you need to preserve the entire image content after deskewing, especially for documents with important content near the edges.
• Impact:
  Enabling may increase output image size and file size. Disabling may crop corners, which is acceptable for documents with wide margins.

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.