Summary of Scene Text Detection Based on Deep Learning

Yuan Li, Mayire Ibrayim, A. Hamdulla
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Abstract

Scene text detection is a general text detection technology, which has become a hot research direction in the field of computer vision and document analysis in recent years, and is widely used in geographic positioning, license plate recognition, unmanned driving, and other fields. Compared with traditional document text detection, scene text changes more dramatically in font, scale, arrangement, and background. Therefore, deep learning technology has become the mainstream method in this field because of its excellent performance, which is helpful to improve the ability of text detection. This paper introduces the main research techniques of natural scene text detection and summarizes the structural characteristics of some classical network models. This paper sorts out, analyzes, and summarizes the running mechanism and performance of various network models for natural scene text detection deep learning-based in recent years. The common public datasets and their application characteristics are listed. Finally, the problems and challenges in scene text detection based on deep learning are discussed and provide an outlook on the future research directions in this field.
基于深度学习的场景文本检测研究综述
场景文本检测是一种通用的文本检测技术,近年来已成为计算机视觉和文档分析领域的一个热点研究方向,广泛应用于地理定位、车牌识别、无人驾驶等领域。与传统的文档文本检测相比,场景文本在字体、比例、排列和背景等方面的变化更大。因此,深度学习技术以其优异的性能成为该领域的主流方法,有助于提高文本检测的能力。介绍了自然场景文本检测的主要研究技术,总结了几种经典网络模型的结构特点。本文对近年来基于深度学习的各种自然场景文本检测网络模型的运行机制和性能进行了梳理、分析和总结。列出了常用的公共数据集及其应用特点。最后,讨论了基于深度学习的场景文本检测中存在的问题和面临的挑战,并对该领域未来的研究方向进行了展望。
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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