DohaScript: A Large-Scale Multi-Writer Dataset for Continuous Handwritten Hindi Text

TL;DR

DohaScript is a large, multi-writer dataset of handwritten Hindi text that captures the complex, continuous nature of Devanagari handwriting, enabling research in recognition, style analysis, and writer identification.

Contribution

It introduces a novel, large-scale, controlled dataset of handwritten Hindi dohas from multiple writers, supporting diverse handwriting analysis tasks.

Findings

High-quality data with demographic metadata

Strong generalization to unseen writers

Reliable benchmark for Devanagari handwriting research

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.