Trace the scribbles: a text restoration and artifact correction network for child handwriting enhancement

Abstract Child handwriting enhancement poses unique challenges distinct from other document restoration tasks. Unlike the global degradation observed in historical manuscripts, children’s handwriting exhibits locally inconsistent faint strokes, misalignment with the writing line, and behavioral artifacts such as scribbles or partial erasures. When scanned, these irregularities are compounded by background noise, illumination variation, and scanner-induced distortions, deforming vital stroke information. Existing document restoration models, optimized for high-contrast ink or uniform degradations, tend to erase these subtle pencil traces, severely limiting their utility for downstream child handwriting analysis. To address these issues, we present TraceNet, an application-driven restoration network designed specifically for faint and irregular child handwriting. TraceNet integrates Swin Transformer-based self-attention within a single deterministic encoder-decoder restoration framework. Temporal diffusion embeddings are used as a conditioning signal to adapt restoration strength according to degradation severity, enabling selective recovery of faint strokes and line removal without over-smoothing. Evaluations on a private corpus of child handwriting show that TraceNet achieves strong performance across image-quality and downstream HTR metrics over state-of-the-art baselines. An additional evaluation on the DIBCO dataset further shows that the same framework is effective for historical document enhancement. Remarkably, the model maintains high restoration quality even under few-shot fine-tuning with only a small set of annotated samples, highlighting its efficiency and adaptability for real-world educational, archival, and behavioral handwriting analysis tasks.