手写文本识别:一项调查
IEEE transactions on pattern analysis and machine intelligence
|December 18, 2025
概括
本调查审查了手写文本识别 (HTR) 模型,从早期方法到高级深度学习. 它涵盖了词级到文档级的识别,为未来的HTR研究提供了路线图.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 手写文本识别 (HTR) 对于模式识别和机器学习至关重要.
- 手写的高变化对稳健的系统开发构成重大挑战.
- HTR的应用范围从历史文档保存到现代数据输入.
研究的目的:
- 为了调查手写文本识别 (HTR) 模型的演变.
- 将HTR研究分类为线级识别和超越线级识别.
- 为了解HTR方法,数据集和挑战提供统一的框架.
主要方法:
- 检查了HTR模型从启发式到深度学习方法的进展.
- 将现有工作分类为行级 (单词,行) 和超越行级 (段落,文档) 的识别.
- 分析研究方法,基准测试,数据集,并报告结果.
主要成果:
- 记录了从文字层面向端到端文档层面的HTR的转变.
- 突出了深度学习技术的进步,以提高HTR准确度.
- 确定了关键数据集和对现场进展的基准评估.
结论:
- HTR已经显著发展,深度学习推动了最先进的性能.
- 未来的研究应该解决文档层面的挑战,并探索新的方法.
- 这项调查为HTR的研究人员和从业人员提供了路线图.
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