Stroke-Based Scene Text Erasing Using Synthetic Data for Training.

Scene text erasing, which replaces text regions with reasonable content in natural images, has drawn significant attention in the computer vision community in recent years. There are two potential subtasks in scene text erasing: text detection and image inpainting. Both subtasks require considerable data to achieve better performance; however, the lack of a large-scale real-world scene-text removal dataset does not allow existing methods to realize their potential. To compensate for the lack of pairwise real-world data, we made considerable use of synthetic text after additional enhancement and subsequently trained our model only on the dataset generated by the improved synthetic text engine. Our proposed network contains a stroke mask prediction module and background inpainting module that can extract the text stroke as a relatively small hole from the cropped text image to maintain more background content for better inpainting results. This model can partially erase text instances in a scene image with a bounding box or work with an existing scene-text detector for automatic scene text erasing. The experimental results from the qualitative and quantitative evaluation on the SCUT-Syn, ICDAR2013, and SCUT-EnsText datasets demonstrate that our method significantly outperforms existing state-of-the-art methods even when they are trained on real-world data.

Graph Neural Networks with Multiple Feature Extraction Paths for Chemical Property Estimation.

Feature extraction is essential for chemical property estimation of molecules using machine learning. Recently, graph neural networks have attracted attention for feature extraction from molecules. However, existing methods focus only on specific structural information, such as node relationship. In this paper, we propose a novel graph convolutional neural network that performs feature extraction with simultaneously considering multiple structures. Specifically, we propose feature extraction paths specialized in node, edge, and three-dimensional structures. Moreover, we propose an attention mechanism to aggregate the features extracted by the paths. The attention aggregation enables us to select useful features dynamically. The experimental results showed that the proposed method outperformed previous methods.

Text Detection Using Multi-Stage Region Proposal Network Sensitive to Text Scale.

Recently, attention has surged concerning intelligent sensors using text detection. However, there are challenges in detecting small texts. To solve this problem, we propose a novel text detection CNN (convolutional neural network) architecture sensitive to text scale. We extract multi-resolution feature maps in multi-stage convolution layers that have been employed to prevent losing information and maintain the feature size. In addition, we developed the CNN considering the receptive field size to generate proposal stages. The experimental results show the importance of the receptive field size.

Automatic Generation of Typographic Font From Small Font Subset.

The automated generation of fonts containing a large number of characters is in high demand. For example, a typical Japanese font requires over 1000 characters. Unfortunately, professional typographers create the majority of fonts, resulting in significant financial and time investments for font generation. The main contribution of this article is the development of a method that automatically generates a target typographic font containing thousands of characters, from a small subset of character images in the target font. We generate characters other than the subset so that a complete font is obtained. We propose a novel font generation method with the capability to deal with various fonts, including a font composed of distinctive strokes, which are difficult for existing methods to handle. We demonstrated the proposed method by generating 2965 characters in 47 fonts. Moreover, objective and subjective evaluations verified that the generated characters are similar to the original characters.

Spectrum Correction Using Modeled Panchromatic Image for Pansharpening.

Pansharpening is a method applied for the generation of high-spatial-resolution multi-spectral (MS) images using panchromatic (PAN) and multi-spectral images. A common challenge in pansharpening is to reduce the spectral distortion caused by increasing the resolution. In this paper, we propose a method for reducing the spectral distortion based on the intensity-hue-saturation (IHS) method targeting satellite images. The IHS method improves the resolution of an RGB image by replacing the intensity of the low-resolution RGB image with that of the high-resolution PAN image. The spectral characteristics of the PAN and MS images are different, and this difference may cause spectral distortion in the pansharpened image. Although many solutions for reducing spectral distortion using a modeled spectrum have been proposed, the quality of the outcomes obtained by these approaches depends on the image dataset. In the proposed technique, we model a low-spatial-resolution PAN image according to a relative spectral response graph, and then the corrected intensity is calculated using the model and the observed dataset. Experiments were conducted on three IKONOS datasets, and the results were evaluated using some major quality metrics. This quantitative evaluation demonstrated the stability of the pansharpened images and the effectiveness of the proposed method.