Leveraging Smart Devices for Scene Text Preserved Image Stylization: A Deep Gaming Approach

Abstract

With the rapid advancement of real-time rendering quality in digital art, the use of computer vision techniques has become popular in producing aesthetically pleasing stylized images of natural scenes. However, the processing time to produce a stylized image is high, especially in resource-constrained environments, such as smart devices. Most stylized imaging approaches are unable to preserve the fine details of the natural scene images, such as texts, symbols, and logos in the painted image, which leads to the loss of significant semantic information. In this article, we propose a fast image stylization framework (digital oil painting) using incremental histogramming that preserves the text content of natural scenes while efficiently painting it in resource-constrained environments. We design a stable multiplayer stochastic game with deep networks to classify regions into text or nontext using deep networks. We propose a multiscale fully convolutional character level text detector (xEASTLite) to detect the presence of a text character or part of a character with high accuracy. We have used different publicly available datasets in smart devices to illustrate the efficacy of the framework.