Uneven illumination and contrast variation are significant challenges in the analysis of historical documents. Uneven illumination degrades optical imaging and affects character recognition in OCR processes, complicating the conversion from grayscale to binary images. Factors such as light scattering contribute to this issue. Additionally, contrast variation, which indicates brightness differences in an image, is influenced by factors like noise and sunlight. This variation makes it difficult for traditional document image analysis algorithms to effectively distinguish foreground text from background, particularly in historical and handwritten documents.
Uneven illumination in optical imaging leads to the diminishing of incident light, making document image analysis, especially character recognition using OCR, challenging. Factors like background objects and light scattering contribute to this problem.
Contrast in an image represents the differences between high and low-intensity pixels. Variations in contrast due to noise or sunlight create challenges for traditional document image analysis algorithms, particularly in recognizing text in historical documents.
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