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What is Binarization?

Binarization converts an image into two value classes, typically foreground and background, using a thresholding method. The threshold may be global or vary across different image regions.

How Binarization works

Binarization maps an intensity or color image into two classes by comparing pixels with a decision threshold. A global method uses one cutoff everywhere, while adaptive methods estimate local thresholds to handle shadows, gradients, or paper texture. The output is a segmentation decision, not merely a monochrome display conversion. Document pipelines place it after normalization or denoising and before OCR, morphology, connected-component analysis, or compression.

Key facts

  1. Global thresholding can work for evenly lit pages with distinct foreground and background histograms, but illumination gradients can merge text with the paper class.
  2. Adaptive methods use neighborhood statistics, improving uneven documents at the cost of more computation and sensitivity to window size and local noise.
  3. Threshold polarity matters: algorithms and file encoders may disagree on whether zero denotes ink or background, causing an apparently inverted mask downstream.

When Binarization matters

Apply binarization before OCR, document cleanup, contour detection, or shape analysis. Poor threshold selection can erase faint text or preserve background noise, especially under uneven lighting.

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