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A Review of Deep Learning Techniques in Document Image Word Spotting
Archives of Computational Methods in Engineering ( IF 9.7 ) Pub Date : 2021-05-17 , DOI: 10.1007/s11831-021-09605-7
Lalita Kumari , Anuj Sharma

From the early days of pattern recognition, word spotting have been important test beds for studying how well machines can perform better decision making. In recent years, word spotting have made dramatic advances with state-of-the-art techniques reaching high level of performance in real life applications. This word spotting domain have driven research by providing suitable yet well-defined challenges for pattern recognition and document analysis practitioners. We continue in this direction by covering extensive literature and new challenges in this domain with comparison of previous work. In particular, we have covered recent deep learning technique role in word spotting and future scope of word spotting with deep learning. We believe writing suitable review of word spotting will not only be crucial for understanding of this field in today era, but also in broader collaborative efforts, especially those with artificial intelligence based tasks. To facilitate future research in word spotting, we have discussed word spotting from learning environment, including its framework design with components as query phase, preprocessing stages, segmentation, feature extraction, feature representation and matching process strategies. Further, deep learning working and use in word spotting architecture has been discussed. The study also include an experimental comparison for the research community to evaluate algorithmic advances along with benchmarked datasets, and future challenges in this field.



中文翻译:

文档图像词点识别中的深度学习技术综述

从模式识别的早期开始,单词识别一直是研究机器如何更好地执行更好决策的重要测试平台。近年来,随着最先进的技术在现实生活中的应用达到了很高的性能水平,单词识别技术取得了巨大的进步。这个词发现领域通过为模式识别和文档分析从业人员提供适当但定义明确的挑战来推动研究。通过与以前的工作进行比较,我们涵盖了该领域的大量文献和新挑战,从而继续朝着这个方向发展。特别是,我们涵盖了最近的深度学习技术在单词发现中的作用以及未来通过深度学习发现单词的范围。我们认为,撰写适当的单词发现评论不仅对于当今时代理解这一领域至关重要,而且还要进行更广泛的协作,尤其是那些具有基于人工智能的任务的协作。为了促进将来的单词发现,我们从学习环境中讨论了单词发现,包括其框架设计,其中包括查询阶段,预处理阶段,分段,特征提取,特征表示和匹配过程策略。此外,已经讨论了深度学习的工作以及在单词发现体系结构中的使用。该研究还包括一个实验性比较,供研究界评估算法的进步以及基准数据集,以及该领域的未来挑战。包括其框架设计,其中包括查询阶段,预处理阶段,分段,特征提取,特征表示和匹配过程策略。此外,已经讨论了深度学习的工作以及在单词发现体系结构中的使用。该研究还包括一个实验性比较,供研究界评估算法的进步以及基准数据集,以及该领域的未来挑战。包括其框架设计,其中包括查询阶段,预处理阶段,分段,特征提取,特征表示和匹配过程策略。此外,已经讨论了深度学习的工作以及在单词发现体系结构中的使用。该研究还包括一个实验性比较,供研究界评估算法的进步以及基准数据集,以及该领域的未来挑战。

更新日期:2021-05-17
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