Advances in OCR for Historical Chinese, Japanese, Coptic, and Greek Texts

This article explores the evolution of optical character recognition (OCR) in deciphering complex historical scripts, including Chinese, Japanese, Coptic, and Greek. It highlights key innovations such as Style Transfer Mapping, Adaptive Gradient Gate, ARCED, and deep reinforcement learning, which reduce error rates and improve recognition accuracy. From reducing Chinese OCR errors by 60% with minimal labeling to achieving 94% accuracy on historical Japanese texts, these breakthroughs demonstrate how AI and deep learning are bridging the gap between ancient manuscripts and modern digital preservation.