场景文本检测中定位的新研究

IF 0.3
P. Sonsare, Rushabh Jain, Rutuj Runwal, Kunal Dave, Ashutosh Banode
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引用次数: 0

摘要

场景文本检测一直是计算机视觉领域的重要研究课题之一。随着深度学习的不断发展和兴起,计算机视觉技术发生了一场影响深远的变革。在深度学习之前的时代,已经有了场景文本检测的算法和技术,但性能一般。近年来,深度学习技术极大地改变了场景文本检测。研究人员已经见证了新发现技术在方法、方法论和整体性能方面的显著进步。本文的重点是总结和分析通过深度学习在场景文本检测方面取得的重大进展。本文涵盖了场景文本检测的介绍,执行场景文本识别和检测的步骤,深度学习之前的技术,最近的技术及其见解,一些结果,以及通过比较算法的概述。我们还将强调使搜索算法成为执行场景文本检测和识别的良好选择的标准,深度学习所包含的显着差异,并分析深度学习之前使用的技术的缺点。本文将有助于理解改变这一领域的关键差异以及一些仍然存在的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel Study on Localization in Scene Text Detection
Scene text detection has been one of the most important topics for research in computer vision. With constant development and rise in deep learning, computer vision technology has undergone an impactful transformation. In the era before deep learning, there existed algorithms and technologies for scene text detection, but the performance was mediocre. In recent years, deep learning technology has remarkably transformed scene text detection. Researchers have witnessed notable advancements in the approach, methodology, and overall performance of the newly discovered techniques. In this paper, the predominant focus is on summarizing and analysing the significant progress in scene text detection through deep learning. This paper covers an introduction to scene text detection, steps to perform scene text recognition and detection, technique before deep-learning, recent techniques and their insights, some results, and an overview by comparing the algorithms. We will also emphasize the criteria that make a search algorithm a good choice for performing scene text detection and recognition, the notable differences incorporated by deep learning, and analyse the drawbacks of the techniques used before deep learning. This paper would be helpful to understand the key differences that have changed this field and also some remaining challenges.
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来源期刊
International Journal of Next-Generation Computing
International Journal of Next-Generation Computing COMPUTER SCIENCE, THEORY & METHODS-
自引率
66.70%
发文量
60
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