Enhancing Digital Documents Using Autoencoders

In the digital age, document and grain denoising are critical for enhancing digital content readability and quality. This paper presents a comprehensive approach to document denoising using Convolutional Conditional Autoencoders. Utilizing datasets like NoisyOffice, Bickley Diary, DIBCO, and Salt & Pepper, the study addresses challenges posed by various document noise types. NoisyOffice, Bickley Diary, and DIBCO datasets offer a broad spectrum of real-world noisy document images, while the Salt & Pepper dataset targets grain denoising. The methodology leverages CCAE capabilities to learn noise patterns and reconstruct clean documents. The model is evaluated with metrics like Mean Squared Error, Peak Signal-to-Noise Ratio, and Structural Similarity Index. Results show significant denoising improvements, validated through quantitative analysis. Comparative studies with existing techniques highlight the proposed model's superiority, providing a robust solution for practical document quality enhancement.