AI-Driven Restoration of Documents using CNN and OCR for Precise Text Recovery

Document restoration is a critical task in preserving text integrity from degraded, noisy, or damaged sources. This paper presents an AI- driven approach utilizing Convolutional Neural Networks (CNN) for image enhancement and Optical Character Recognition (OCR) for precise text recovery. The CNN model effectively removes noise, reconstructs missing or distorted text regions, and enhances readability, while OCR ensures accurate transcription. The proposed method demonstrates superior performance compared to traditional restoration techniques, improving both visual clarity and text extraction accuracy.Extensive experiments validate the effectiveness of this approach across various document types, including handwritten, printed, and scanned materials. The model is trained on diverse datasets to handle variations in font styles, ink smudging, and document aging effects. Comparative analysis with existing methods highlights the robustness of our approach in restoring fine details while minimizing artifacts. This work contributes to advancing automated document restoration, making texts more accessible for digital archiving, analysis, and research, with potential applications in historical preservation, legal documentation, and academic studies. Key words: Document Restoration, CNN, OCR, Text Recovery, Image Enhancement, Noise Removal, Document Denoising, Pattern Recognition.