  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.
