EnsExam: A Dataset for Handwritten Text Erasure on Examination Papers

Abstract

Handwritten text erasure on examination papers is an important new research topic with high practical value due to its ability to restore examination papers and collect questions that are answered incorrectly for review, thereby improving educational efficiency. However, to the best of our knowledge, there is no publicly available dataset for handwritten text erasure on examination papers. To facilitate the development of this field, we build a real-world dataset called SCUT-EnsExam (short for EnsExam). The dataset consists of 545 examination paper images, each of which has been carefully annotated to provide a visually reasonable erasure target. With EnsExam, we propose an end-to-end model, which introduces a soft stroke mask to erase the handwritten text precisely. Furthermore, we propose a simple yet effective loss called stroke normalization (SN) loss to alleviate the imbalance between text and non-text regions. Extensive numerical experiments shows that our proposed method outperforms previous state-of-the-art methods on EnsExam. In addition, quantitative experiments on scene text removal benchmark, SCUT-EnsText, demonstrate the generalizability of our method. The EnsExam will be made available at https://github.com/SCUT-DLVCLab/SCUT-EnsExam.