Author(s)

Runqing Yan ¹, Jianye An ¹

Affiliation(s)

¹ Tianjin University of Commerce, Tianjin, China

Corresponding Author

Jianye An

ABSTRACT

With the growing demand for paperless offices and digital archiving, many paper documents are scanned into image formats for management and dissemination. These images often contain handwritten annotations, signatures, or markings, which interfere with accurate understanding and automatic analysis, especially in educational scenarios where exams and assignments include extensive handwritten content. This highlights the need for effective handwritten text removal techniques. This work proposes an end-to-end handwritten text removal method based on a U-Net enhanced with ResNetV2 modules. The model leverages multi-scale feature extraction, residual learning, and skip connections to remove handwritten marks while preserving printed text and document layout. In addition, a high-quality, large-scale handwritten text removal dataset is constructed and publicly released to provide a standardized benchmark for evaluation and reproducibility. Experimental results show that the proposed approach efficiently removes handwritten traces while maintaining document structure and visual consistency, improving the usability of digital documents. The study contributes to research on handwritten text removal and provides technical support for educational resource digitization, smart learning, and document management.

KEYWORDS

ResNetV2 Fusion, U-Net, Handwriting Erase, Removal of Handwritten Text

CITE THIS PAPER

Runqing Yan, Jianye An. U-Net Handwriting Removal Method and Dataset with ResNetV2 Fusion. Journal of Image Processing Theory and Applications (2026) Vol. 9, No.1, 14-21. DOI: http://dx.doi.org/10.23977/jipta.2026.090102.

REFERENCES

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