  Scene text editing (STE) aims to replace text with the desired one while
preserving background and styles of the original text. However, due to the
complicated background textures and various text styles, existing methods fall
short in generating clear and legible edited text images. In this study, we
attribute the poor editing performance to two problems: 1) Implicit decoupling
structure. Previous methods of editing the whole image have to learn different
translation rules of background and text regions simultaneously. 2) Domain gap.
Due to the lack of edited real scene text images, the network can only be well
trained on synthetic pairs and performs poorly on real-world images. To handle
the above problems, we propose a novel network by MOdifying Scene Text image at
strokE Level (MOSTEL). Firstly, we generate stroke guidance maps to explicitly
indicate regions to be edited. Different from the implicit one by directly
modifying all the pixels at image level, such explicit instructions filter out
the distractions from background and guide the network to focus on editing
rules of text regions. Secondly, we propose a Semi-supervised Hybrid Learning
to train the network with both labeled synthetic images and unpaired real scene
text images. Thus, the STE model is adapted to real-world datasets
distributions. Moreover, two new datasets (Tamper-Syn2k and Tamper-Scene) are
proposed to fill the blank of public evaluation datasets. Extensive experiments
demonstrate that our MOSTEL outperforms previous methods both qualitatively and
quantitatively. Datasets and code will be available at
https://github.com/qqqyd/MOSTEL.
