Document Image Rectification

Document image rectification aims to computationally correct distortions in scanned or photographed documents, improving readability and enabling accurate optical character recognition (OCR). Recent research emphasizes learning-based approaches, employing architectures like transformers and autoencoders to learn robust representations of document structure from both complete and partial images, even those with complex creases or folds. This focus on handling diverse distortions and improving rectification accuracy for challenging scenarios is crucial for advancing document digitization and text analysis applications.