Abstract
Despite having hundreds of millions of speakers, handwritten Devanagari text remains severely underrepresented in publicly available benchmark datasets. Existing resources are limited in scale, focus primarily on isolated characters or short words, and lack controlled lexical content and writer level diversity, which restricts their utility for modern data driven handwriting analysis. As a result, they fail to capture the continuous, fused, and structurally complex nature of Devanagari handwriting, where characters are connected through a shared shirorekha (horizontal headline) and exhibit rich ligature formations. We introduce DohaScript, a large scale, multi writer dataset of handwritten Hindi text collected from 531 unique contributors. The dataset is designed as a parallel stylistic corpus, in which all writers transcribe the same fixed set of six traditional Hindi dohas (couplets). This controlled design enables systematic analysis of writer specific variation independent of linguistic content, and supports tasks such as handwriting recognition, writer identification, style analysis, and generative modeling. The dataset is accompanied by non identifiable demographic metadata, rigorous quality curation based on objective sharpness and resolution criteria, and page level layout difficulty annotations that facilitate stratified benchmarking. Baseline experiments demonstrate clear quality separation and strong generalization to unseen writers, highlighting the dataset's reliability and practical value. DohaScript is intended to serve as a standardized and reproducible benchmark for advancing research on continuous handwritten Devanagari text in low resource script settings.
Abstract (translated)
尽管有数亿使用者，手写的德瓦纳加里文本在公开可用的基准数据集中仍然严重不足。现有的资源规模有限，主要集中在孤立字符或短词上，并且缺乏控制词汇内容和书写者多样性，这限制了它们在现代基于数据的手写分析中的实用性。因此，这些数据集无法捕捉到德瓦纳加里手写的连续性、融合性和结构复杂性的特点，在这种文本中，字符通过共享的水平标题（shirorekha）连接，并且呈现出丰富的连字形式。 我们引入了DohaScript，这是一个大规模、多书写者的手写印地文数据集，由531位独特的贡献者提供。该数据集被设计为平行风格语料库，在其中所有书写者都会转录六组传统的印地文双行诗（dohas）。这种受控的设计能够系统分析独立于语言内容的特定书写者的变异性，并支持诸如手写识别、书写者鉴别、风格分析和生成模型等任务。该数据集还附带了不可识别的人口统计数据，基于客观清晰度和分辨率标准进行严格的质量筛选，并提供了页面级别的布局难度注释，以促进分层基准测试。 基线实验显示出了明显的质量差异，并且在未见过的书写者上具有强大的泛化能力，突显了该数据集的可靠性和实际价值。DohaScript旨在作为低资源脚本环境中推进连续手写德瓦纳加里文本研究的标准和可重复基准。
URL
https://arxiv.org/abs/2602.18089
