How does the system handle scans and low-quality images?

A system working with scans and low-quality images typically employs several approaches to improve quality and ensure proper processing:

• Image preprocessing: noise filtering, contrast enhancement, brightness alignment.
• Using quality enhancement algorithms: super-resolution, interpolation, smoothing.
• Optical Character Recognition (OCR) with adaptation: applying models trained on low-quality data or using neural network methods resilient to noise.
• Post-processing of results: correcting recognition errors using dictionaries, contextual analysis.

For example, if the system recognizes text in scans, it may first apply a Gaussian filter to remove noise, then enhance sharpness, and subsequently run OCR. If the quality is too low, a deep learning model trained on similar images might be employed to improve recognition accuracy.