Abstract
Handwritten Text Recognition (HTR) has become an essential field within pattern recognition and machine learning, with applications spanning historical document preservation to modern data entry and accessibility solutions. The complexity of HTR lies in the high variability of handwriting, which makes it challenging to develop robust recognition systems. This survey examines the evolution of HTR models, tracing their progression from early heuristic-based approaches to contemporary state-of-the-art neural models, which leverage deep learning techniques. The scope of the field has also expanded, with models initially capable of recognizing only word-level content progressing to recent end-to-end document-level approaches. Our paper categorizes existing work into two primary levels of recognition: (1) \emph{up to line-level}, encompassing word and line recognition, and (2) \emph{beyond line-level}, addressing paragraph- and document-level challenges. We provide a unified framework that examines research methodologies, recent advances in benchmarking, key datasets in the field, and a discussion of the results reported in the literature. Finally, we identify pressing research challenges and outline promising future directions, aiming to equip researchers and practitioners with a roadmap for advancing the field.
Abstract (translated)
手写文本识别（HTR）已成为模式识别和机器学习领域的一个重要分支，其应用范围从历史文档的保存到现代数据录入及无障碍解决方案。HTR 的复杂性在于手写风格的高度变异性，这使得开发稳健的识别系统具有挑战性。本文综述了 HTR 模型的发展历程，追溯其从早期基于启发式的方法演进至当前最先进的神经网络模型的过程，后者利用深度学习技术来提升性能。该领域的研究范围也已扩大，初期只能识别单词级内容的模型逐渐发展为现今涵盖整个文档级别的端到端方法。我们在论文中将现有的工作分类为两个主要的识别层次：（1）**线级别及以下**，包括词和行的识别；以及 （2）**超出行级别**，解决段落和整篇文档层面的问题。我们提供了一个统一的研究框架，涵盖了研究方法、近期基准测试的进步、领域中的关键数据集，以及对文献中报告结果的讨论。最后，我们指出了亟待解决的研究挑战，并概述了未来有前景的发展方向，旨在为研究人员及从业者提供一份推进该领域的路线图。
URL
https://arxiv.org/abs/2502.08417
