Designing a Modular Framework for Processing and Enhancing Scanned Documents Using Advanced Denoising Algorithms - Zahraa Ali Mohamed Nather; Hasan Maher Ahmed (Journal of Al-Qadisiyah for Computer Science and Mathematics, 2026)
Scanned documents are often flawed such as background noise, skew and uneven illuminations which negatively affect reading and text recognition. The given study presents a two-step processing model that can serve to improve the quality of grayscale and color-scanned documents because of the use of combined denoising and deskewing methods. Three denoising methods were tested under various noise levels: DRUNet, DnCNN, and Total Variation (TV). To get the best results in restoring image quality, we used pre-trained models for the deep learning algorithms (DRUNet and DnCNN) through the DeepInv library. This allowed us to use powerful, ready-to-use features to clean the documents effectively. Their performance was then measured using standard quality scores and visual checks. The results showed that DRUNet produced the best and more reproducible performance, which was able to suppress noise and preserve fine structural and textual fidelity. In addition, preprocessing step of Otsu thresholding and minimum bounding rectangle estimation was also applied to automatically correct document skew to enhance text alignment and readability. Python and Gradio were used as the implementation language of the system to offer an interactive, transparent, and reproducible platform. In general, the suggested framework would significantly increase the clarity, alignment, and the overall quality of scanned documents and make them more reliable to use in OCR and digital archiving purposes.