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.

中文翻译：

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利用智能设备进行场景文本保留的图像样式化：一种深度游戏方法

随着数字艺术中实时渲染质量的快速提高，计算机视觉技术的使用在生成自然场景中美学上令人愉悦的风格化图像方面已变得很流行。但是，生成样式化图像的处理时间很高，尤其是在资源受限的环境（例如智能设备）中。大多数风格化的成像方法无法保留自然场景图像的精细细节，例如绘画图像中的文本，符号和徽标，这会导致大量语义信息的丢失。在本文中，我们提出了一种使用增量直方图的快速图像样式化框架（数字油画），该框架保留了自然场景的文本内容，同时在资源受限的环境中有效地对其进行了绘画。我们设计了具有深度网络的稳定的多人随机游戏，以使用深度网络将区域分为文本或非文本。我们提出一种多尺度全卷积字符级文本检测器（xEASTLite），以高精度检测文本字符或部分字符的存在。我们在智能设备中使用了不同的公开可用数据集来说明该框架的有效性。