Text Restoration

Text restoration aims to recover damaged or incomplete text from various sources, including ancient manuscripts, degraded images, and digitally obscured documents. Current research focuses on leveraging deep learning models, such as recurrent neural networks (RNNs) and large language models (LLMs), often incorporating multimodal approaches that combine textual and visual information to improve accuracy. These advancements are significantly impacting fields like digital humanities, enabling more accurate transcriptions of historical artifacts and facilitating the preservation of cultural heritage. Furthermore, improvements in image restoration techniques are being achieved by leveraging textual representations to guide the process, leading to better results across various image degradation types.