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

Kunwar Arpit Singh; Ankush Prakash; Haroon R Lone

cs.CVFeb 20, 2026

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

Authors: Kunwar Arpit Singh, Ankush Prakash, Haroon R Lone

Organizations: IISER Bhopal, Bhopal, India

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.

Figures & tables

Dataset	Script(s)	Page-level Samples (# Pages)	# Writers	Text Type	Demographics
IIIT-HW-Words ( Gongidi and Jawahar, 2021 )	Indic (multi)	– (word-only dataset)	135	Isolated words	No
IIIT-HW-Dev ( Dutta et al., 2018 )	Hindi (Devanagari)	– (word-only dataset)	12	Continuous words	Limited
Numerals/Vowels Dataset ( Prashanth et al., 2022 )	Devanagari	– (character-only dataset)	$\sim$ 3800 subjects	Isolated characters	Yes
CHIPS (PLATTER) ( Kasuba et al., 2025 )	Indic (multi)	25,290 pages	–	Continuous	No
Parimal Hindi ( Dey et al., 2025 )	Hindi (Devanagari)	500 pages	10	Continuous paragraphs	Yes
AnciDev ( Sharma et al., 2025 )	Ancient Devanagari	500 pages	–	Continuous lines	No

\fontspec_if_language:nTF ENG \addfontfeature Language=English Table 1 . Quantitative comparison of existing Indic handwriting datasets with the proposed dataset.

\fontspec_if_language:nTF ENG \addfontfeature Language=English Figure 1 . Text corpus comprising six dohas used in the study.

\fontspec_if_language:nTF ENG \addfontfeature Language=English Figure 2 . Comprehensive coverage of Devanagari characters in the selected dohas , including consonants by articulation category, vowel classes, vowel diacritics (mātrās), halant, and common conjuncts.

\fontspec_if_language:nTF ENG \addfontfeature Language=English Figure 3 . Age distribution of the 531 participants in the dataset. The dashed line indicates the median age. The detailed distribution is shown in Table \fontspec_if_language:nTFENG\addfontfeatureLanguage=English8 in the Appendix \fontspec_if_language:nTFENG\addfontfeatureLanguage=EnglishB .

\fontspec_if_language:nTF ENG \addfontfeature Language=English Figure 4 . Frequency distribution of handwriting samples across states of India. The tabulated state wise distribution is show in Table \fontspec_if_language:nTFENG\addfontfeatureLanguage=English7 in Appendix \fontspec_if_language:nTFENG\addfontfeatureLanguage=EnglishA .

Panel of Figure 6 — \fontspec_if_language:nTF ENG \addfontfeature Language=English Figure 5 . Sample images from the dataset. Each segment corresponds to one handwritten page instance.

\fontspec_if_language:nTF ENG \addfontfeature Language=English Figure 6 . Blur score distribution with initial binary threshold labeling (before CNN refinement).

\fontspec_if_language:nTF ENG \addfontfeature Language=English Figure 7 . Blur score distribution by quality class. Boxes show inter quartile ranges with medians (horizontal lines) and means (green triangles). Outliers appear as circles. Class separation validates stratification, while MediumGood overlap explains reduced classification performance in boundary regions.

\fontspec_if_language:nTF ENG \addfontfeature Language=English Figure 8 . CNN architecture for handwriting quality classification. Three convolutional blocks extract hierarchical features, followed by adaptive pooling and fully connected layers for quality prediction.

Model	Classes	Test Accuracy	Val. Accuracy
CNN (Binary)	2	96.26%	98.82%
CNN (Four-class)	4	85.98%	80.00%

\fontspec_if_language:nTF ENG \addfontfeature Language=English Table 2 . Overall quality classification performance

Model	Class	Prec.	Rec.	F1
CNN (Binary)	Low-Med. ( $<$ 3000)	0.94	0.99	0.96
CNN (Binary)	High ( $\geq$ 3000)	0.99	0.93	0.96
CNN (Four-class)	Low ( $<$ 1000)	0.96	0.90	0.93
	Medium (1–3k)	0.75	0.83	0.79
	Good (3–5k)	0.93	0.81	0.87
	Excellent ( $\geq$ 5k)	0.78	0.89	0.83

\fontspec_if_language:nTF ENG \addfontfeature Language=English Table 3 . Per-class performance on test set (107 samples)

Panel of Figure 12 — \fontspec_if_language:nTF ENG \addfontfeature Language=English Figure 9 . Representative handwritten pages from each segmentation difficulty class. Easy samples exhibit clear inter-line separation, Medium pages contain moderate spacing variation, and Complex pages show overlap and irregular baselines leading to ambiguous line boundaries.

Metric	Value
Total Documents	531
Perfect Segmentation (12/12 lines)	157 (29.57%)
Mean Segmentation Score	43.46 $\pm$ 22.55
95% CI	[41.54, 45.39]
Median Score	42.91
Mean Lines Detected	11.22 $\pm$ 2.92

\fontspec_if_language:nTF ENG \addfontfeature Language=English Table 4 . Line segmentation performance on DohaScript (N=531).

Difficulty	Count	Percentage	Score Range
Easy	110	20.7%	65.1–91.8
Medium	141	26.6%	45.9–66.4
Complex	280	52.7%	7.6–46.7

\fontspec_if_language:nTF ENG \addfontfeature Language=English Table 5 . Distribution of document segmentation difficulty.

Error (lines)	Count	Percentage
0	157	29.6%
1	138	26.0%
2	69	13.0%
3	64	12.1%
4	28	5.3%
5	28	5.3%

\fontspec_if_language:nTF ENG \addfontfeature Language=English Table 6 . Distribution of line detection errors.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

S.No.	State	Participants
1	Andhra Pradesh	15
2	Arunachal Pradesh	1
3	Assam	3
4	Bihar	150
5	Chandigarh	2
6	Chhattisgarh	3

Appendix

\fontspec_if_language:nTF ENG \addfontfeature Language=EnglishTable 7. State-wise distribution of participants.

Age	Participants	Age	Participants
7	4	19	99
8	4	20	76
9	14	21	70
10	2	22	47
11	7	23	6
12	9	24	3

Appendix

\fontspec_if_language:nTF ENG \addfontfeature Language=EnglishTable 8. Distribution of participants across different age groups.

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