Abstract

Textual information in a captured scene plays an important role in scene interpretation and decision making. Though there exist methods that can successfully detect and interpret complex text regions present in a scene, to the best of our knowledge, there is no significant prior work that aims to modify the textual information in an image. The ability to edit text directly on images has several advantages including error correction, text restoration and image reusability. In this paper, we propose a method to modify text in an image at character-level. We approach the problem in two stages. At first, the unobserved character (target) is generated from an observed character (source) being modified. We propose two different neural network architectures - (a) FANnet to achieve structural consistency with source font and (b) Colornet to preserve source color. Next, we replace the source character with the generated character maintaining both geometric and visual consistency with neighboring characters. Our method works as a unified platform for modifying text in images. We present the effectiveness of our method on COCO-Text and ICDAR datasets both qualitatively and quantitatively.

Network Architecture

Click on the image for a detailed view of the network architecture.

Editing Results

Each image pair consists of the original image (Left) and the edited image (Right).

Paper and Supplementary Materials

Download Paper ~8MB PDF

Download Supplementary Materials ~6MB PDF

Publication
@ CVF Open Access

Code
@ GitHub

Datasets + Models
@ Google Drive

Datasets + Kernels
@ Kaggle

Citation

@InProceedings{Roy_2020_CVPR,
  title     = {STEFANN: Scene Text Editor using Font Adaptive Neural Network},
  author    = {Roy, Prasun and Bhattacharya, Saumik and Ghosh, Subhankar and Pal, Umapada},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2020}
}