# Scene Text Eraser

## TL;DR

This paper introduces a CNN-based scene text erasing method that effectively removes text from natural images, reducing the risk of personal information leakage and improving privacy preservation.

## Contribution

The work presents a novel CNN model for scene text erasing that outperforms existing methods in privacy protection by effectively filling in text regions with background colors.

## Key findings

- Significant decrease in text detection precision after erasing
- Effective removal of text in natural scene images
- Validated on ICDAR2013 dataset

## Abstract

The character information in natural scene images contains various personal information, such as telephone numbers, home addresses, etc. It is a high risk of leakage the information if they are published. In this paper, we proposed a scene text erasing method to properly hide the information via an inpainting convolutional neural network (CNN) model. The input is a scene text image, and the output is expected to be text erased image with all the character regions filled up the colors of the surrounding background pixels. This work is accomplished by a CNN model through convolution to deconvolution with interconnection process. The training samples and the corresponding inpainting images are considered as teaching signals for training. To evaluate the text erasing performance, the output images are detected by a novel scene text detection method. Subsequently, the same measurement on text detection is utilized for testing the images in benchmark dataset ICDAR2013. Compared with direct text detection way, the scene text erasing process demonstrates a drastically decrease on the precision, recall and f-score. That proves the effectiveness of proposed method for erasing the text in natural scene images.