Development of software for the segmentation of text areas in real-scene images

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-10-01 DOI:10.18287/2412-6179-co-1047

V. A. Lobanova, Yuliya Ivanova

引用次数: 0

Abstract

This article discusses the design and development of a neural network algorithm for the segmentation of text areas in real-scene images. After reviewing the available neural network models, the U-net model was chosen as a basis. Then an algorithm for detecting text areas in real-scene images was proposed and implemented. The experimental training of the network allows one to define the neural network parameters such as the size of input images and the number and types of the network layers. Bilateral and low-pass filters were considered as a preprocessing stage. The number of images in the KAIST Scene Text Database was increased by applying rotations, compression, and splitting of the images. The results obtained were found to surpass competing methods in terms of the F-measure value.

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实景图像文本区域分割软件的开发

本文讨论了一种用于真实场景图像文本区域分割的神经网络算法的设计和开发。在回顾了现有的神经网络模型后，选择U-net模型作为基础。在此基础上，提出并实现了一种实景图像文本区域检测算法。网络的实验训练允许人们定义神经网络参数，如输入图像的大小和网络层的数量和类型。双边滤波器和低通滤波器被认为是预处理阶段。通过对图像进行旋转、压缩和分割，增加了KAIST场景文本数据库中的图像数量。所获得的结果被发现在f测量值方面优于竞争方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.