Reimagining CRNN with Attention for Handwritten Chinese Text Recognition in Noisy Backgrounds

Real-world handwritten documents often contain noise and complex elements, such as notes with colored markings, naturally degraded handwriting, and diverse paper backgrounds. Based on the strong demand for techniques that convert text images into editable digital formats, this study focuses on recognizing line-level handwritten Chinese text in complex backgrounds to improve recognition accuracy. Through a comparative analysis of five experimental settings, including no preprocessing, different preprocessing techniques, and advanced enhancement methods leveraging the self-attention mechanism from the transformer network, our reimagined CRNN model achieves the highest accuracy. These results confirm the effectiveness of the selfattention mechanism in boosting recognition performance under challenging conditions, offering valuable insights for future advancements in handwritten text recognition technologies.