Table of Contents
Morphological operations are image processing techniques that modify the structure of objects within an image. They are widely used in document image cleanup to improve readability and prepare images for OCR (Optical Character Recognition). This article presents real-world examples of how these operations are applied to enhance document images.
Removing Noise and Small Artifacts
One common application is removing small noise particles that appear as specks or dots in scanned documents. Using morphological opening, small objects are eliminated without affecting the main text. This process involves erosion followed by dilation, which effectively cleans up the image.
Closing Gaps in Text
Closing gaps within characters or between parts of text improves clarity. Morphological closing, which involves dilation followed by erosion, fills small holes and connects broken strokes. This is particularly useful for scanned documents with faded or broken text.
Enhancing Text Segmentation
Morphological operations assist in segmenting text from the background. For example, dilation can expand text regions, making them more distinct from the background noise. Conversely, erosion can separate connected characters or lines, aiding in character recognition.
Common Morphological Operations Used
- Erosion: Removes pixels on object boundaries, reducing noise.
- Dilation: Adds pixels to object boundaries, filling gaps.
- Opening: Erosion followed by dilation, used for noise removal.
- Closing: Dilation followed by erosion, used for gap filling.