The Surprisingly Straightforward Scene Text Removal Method With Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis

Hyeonsu Lee, Chankyu Choi
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引用次数: 5

Abstract

Scene text removal (STR), a task of erasing text from natural scene images, has recently attracted attention as an important component of editing text or concealing private information such as ID, telephone, and license plate numbers. While there are a variety of different methods for STR actively being researched, it is difficult to evaluate superiority because previously proposed methods do not use the same standardized training/evaluation dataset. We use the same standardized training/testing dataset to evaluate the performance of several previous methods after standardized re-implementation. We also introduce a simple yet extremely effective Gated Attention (GA) and Region-of-Interest Generation (RoIG) methodology in this paper. GA uses attention to focus on the text stroke as well as the textures and colors of the surrounding regions to remove text from the input image much more precisely. RoIG is applied to focus on only the region with text instead of the entire image to train the model more efficiently. Experimental results on the benchmark dataset show that our method significantly outperforms existing state-of-the-art methods in almost all metrics with remarkably higher-quality results. Furthermore, because our model does not generate a text stroke mask explicitly, there is no need for additional refinement steps or sub-models, making our model extremely fast with fewer parameters. The dataset and code are available at this https://github.com/naver/garnet.
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具有门控注意和兴趣区域生成的令人惊讶的简单场景文本去除方法:一个全面的突出模型分析
场景文本删除(Scene text removal, STR)是一种从自然场景图像中删除文本的技术,近年来作为编辑文本或隐藏身份证件、电话号码、车牌号码等私人信息的重要组成部分而受到关注。虽然有各种不同的STR方法正在被积极研究,但很难评估其优越性,因为之前提出的方法没有使用相同的标准化训练/评估数据集。我们使用相同的标准化训练/测试数据集来评估标准化后重新实现的几种先前方法的性能。本文还介绍了一种简单但非常有效的门控注意(GA)和兴趣区域生成(RoIG)方法。GA将注意力集中在文本笔画以及周围区域的纹理和颜色上,从而更精确地从输入图像中删除文本。RoIG应用于只关注有文本的区域,而不是整个图像,以更有效地训练模型。在基准数据集上的实验结果表明,我们的方法在几乎所有指标上都明显优于现有的最先进的方法,并且结果质量显著提高。此外,由于我们的模型不显式地生成文本笔画遮罩,因此不需要额外的细化步骤或子模型,从而使我们的模型以更少的参数非常快。数据集和代码可在此获得https://github.com/naver/garnet。
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