Text Segmentation under Non-uniform Illumination

For text segmentation under non-uniform illumination conditions, we can comprehensively utilize homomorphic filtering, Bernsen local thresholding, and edge detection methods to enhance segmentation accuracy and robustness. Homomorphic filtering effectively addresses illumination variations by separating lighting and reflectance components in the frequency domain, typically implemented using Fourier transform and Gaussian high-pass filters. Bernsen local thresholding adaptively determines thresholds based on local image characteristics by calculating contrast within sliding windows, making it particularly effective for uneven lighting scenarios. Edge detection methods such as Canny or Sobel operators help capture text boundary information by identifying intensity gradients, further improving segmentation precision. By integrating these approaches in a pipeline - first applying homomorphic filtering for illumination correction, then using Bernsen for adaptive binarization, and finally employing edge detection for boundary refinement - we achieve more accurate and stable text segmentation under challenging lighting conditions.