Datasets:
dataset_info:
- config_name: absolute
features:
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dtype: int64
- name: referenceSentenceId
dtype: int64
- name: annotator8
dtype: int32
- name: annotator9
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dtype: int32
- name: samplePath
dtype: large_string
splits:
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num_examples: 391
- name: validation
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download_size: 18579
dataset_size: 40534
- config_name: absolute_pre_study
features:
- name: sampleId
dtype: int64
- name: referenceSentenceId
dtype: int64
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dtype: int32
- name: annotator42
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dtype: int32
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dtype: int32
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download_size: 0
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- config_name: absolute_test
features:
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dtype: int64
- name: referenceSentenceId
dtype: int64
- name: annotator8
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- name: annotator11
dtype: int32
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dtype: large_string
splits:
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num_examples: 391
download_size: 0
dataset_size: 20503
- config_name: absolute_val
features:
- name: sampleId
dtype: int64
- name: referenceSentenceId
dtype: int64
- name: annotator8
dtype: int32
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dtype: int32
- name: annotator10
dtype: int32
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dtype: int32
- name: samplePath
dtype: large_string
splits:
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num_examples: 382
download_size: 9220
dataset_size: 20031
- config_name: aspect_ratio
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- name: aspectRatio
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- config_name: characteristics
features:
- name: sampleId
dtype: int64
- name: text
dtype: large_string
- name: referenceSentenceId
dtype: int64
- name: isWrittenInPureCursive
dtype: bool
- name: isStrokeThin
dtype: bool
- name: containsTypo
dtype: bool
- name: containsCorrection
dtype: bool
- name: samplePath
dtype: large_string
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- name: test
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- config_name: default
features:
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dtype: int64
- name: sampleId2
dtype: int64
- name: referenceSentenceId1
dtype: int64
- name: referenceSentenceId2
dtype: int64
- name: questionId
dtype: int64
- name: score
dtype: int64
- name: submissionTimestamp
dtype: large_string
- name: samplePath1
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- name: samplePath2
dtype: large_string
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num_examples: 11340
- name: test
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- name: validation
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dataset_size: 2346621
- config_name: main_study
features:
- name: userId
dtype: int64
- name: sampleId1
dtype: int64
- name: sampleId2
dtype: int64
- name: referenceSentenceId1
dtype: int64
- name: referenceSentenceId2
dtype: int64
- name: questionId
dtype: int64
- name: score
dtype: int64
- name: submissionTimestamp
dtype: large_string
- name: samplePath1
dtype: large_string
- name: samplePath2
dtype: large_string
splits:
- name: train
num_bytes: 1395322
num_examples: 11340
- name: test
num_bytes: 481201
num_examples: 3910
- name: validation
num_bytes: 470098
num_examples: 3820
download_size: 333435
dataset_size: 2346621
- config_name: pre_study
features:
- name: userId
dtype: int64
- name: sampleId1
dtype: int64
- name: sampleId2
dtype: int64
- name: referenceSentenceId1
dtype: int64
- name: referenceSentenceId2
dtype: int64
- name: questionId
dtype: int64
- name: score
dtype: int64
- name: submissionTimestamp
dtype: large_string
- name: samplePath1
dtype: large_string
- name: samplePath2
dtype: large_string
splits:
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- name: annotator12
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num_examples: 1530
- name: annotator2
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num_examples: 1606
- name: annotator3
num_bytes: 189783
num_examples: 1541
download_size: 102290
dataset_size: 802503
configs:
- config_name: absolute
data_files:
- split: test
path: absolute/test-*
- split: validation
path: absolute/validation-*
- config_name: absolute_pre_study
data_files:
- split: train
path: absolute_pre_study/train-*
- config_name: absolute_test
data_files:
- split: train
path: absolute_test/train-*
- config_name: absolute_val
data_files:
- split: train
path: absolute_val/train-*
- config_name: aspect_ratio
data_files:
- split: train
path: aspect_ratio/train-*
- split: test
path: aspect_ratio/test-*
- split: validation
path: aspect_ratio/validation-*
- config_name: characteristics
data_files:
- split: train
path: characteristics/train-*
- split: test
path: characteristics/test-*
- split: validation
path: characteristics/validation-*
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
- config_name: main_study
data_files:
- split: train
path: main_study/train-*
- split: test
path: main_study/test-*
- split: validation
path: main_study/validation-*
- config_name: pre_study
data_files:
- split: annotator11
path: pre_study/annotator11-*
- split: annotator12
path: pre_study/annotator12-*
- split: annotator2
path: pre_study/annotator2-*
- split: annotator3
path: pre_study/annotator3-*
license: mit
language:
- de
tags:
- handwriting
- legibility
- pairwise-comparison
- ranking
- vision
size_categories:
- 10K<n<100K
task_categories:
- text-classification
HaLO: Handwriting Assessment for Legibility and Ordering
HaLO is a dataset of 1,907 handwriting images from 202 German schoolchildren (ages 10--12), annotated with 19,070 pairwise legibility judgments. It supports research on AI-based handwriting legibility assessment through comparative ranking.
Dataset Description
Each handwriting sample depicts one of ten predefined German sentences (20--27 characters each), written by a child in their natural handwriting style. The samples were scanned and cropped from paper sheets. Due to privacy constraints, only Pixtral 12B image embeddings (1024-dimensional feature vectors) are released, not the raw images.
Annotation
Legibility annotations were collected using a pairwise comparison paradigm: annotators were shown two handwriting samples side by side and asked to select the more legible one. A pre-study compared this approach with absolute Likert-scale ratings and found that pairwise comparisons produce higher inter- and intra-rater agreement. The main study annotations were collected by eight annotators (three experts, five non-experts).
Data Splits
The dataset is split into training (1,134 samples, 121 children), validation (382 samples, 40 children), and test (391 samples, 41 children) partitions. All samples from the same child are assigned to the same partition to prevent information leakage.
Configurations
default / main_study
Pairwise legibility annotations from the main study. Each row is a comparison between two samples.
| Field | Description |
|---|---|
sampleId1, sampleId2 |
IDs of the two compared samples |
score |
1 if sample 1 is more legible, -1 if sample 2 is more legible |
samplePath1, samplePath2 |
Paths to the corresponding feature .npy files |
userId |
Annotator ID |
referenceSentenceId1, referenceSentenceId2 |
Reference sentence IDs (1--10) |
questionId |
Annotation question ID |
submissionTimestamp |
Timestamp of the annotation |
Splits: train (11,340), test (3,910), validation (3,820)
from datasets import load_dataset
dataset = load_dataset("MarcoLents/HaLO")
pre_study
Pairwise legibility annotations from the annotation pre-study, with the same schema as the main study. Splits correspond to individual annotators, where Annotator 1 performed the procedure twice (splits annotator11 and annotator12).
Splits: annotator11 (1,839), annotator12 (1,530), annotator2 (1,606), annotator3 (1,541)
pre_study = load_dataset("MarcoLents/HaLO", "pre_study")
characteristics
Binary sample characteristics annotated per sample.
| Field | Description |
|---|---|
sampleId |
Sample ID |
text |
Transcribed text content |
referenceSentenceId |
Reference sentence ID |
isWrittenInPureCursive |
Whether the sample is written entirely in cursive |
isStrokeThin |
Whether the stroke width is predominantly thin |
containsTypo |
Whether at least one typographical error is present |
containsCorrection |
Whether at least one correction is present |
samplePath |
Path to the feature .npy file |
Splits: train (1,134), test (391), validation (382)
characteristics = load_dataset("MarcoLents/HaLO", "characteristics")
absolute_pre_study
Absolute legibility ratings on a 5-point Likert scale (0 = very legible, 4 = not legible at all) from the annotation pre-study. Four annotators independently rated a random subset of 136 samples. Annotator 4 rated twice (annotator41, annotator42).
| Field | Description |
|---|---|
sampleId |
Sample ID |
referenceSentenceId |
Reference sentence ID |
annotator41 -- annotator7 |
Integer Likert ratings (0--4) per annotator |
samplePath |
Path to the feature .npy file |
Splits: train (136)
absolute_pre_study = load_dataset("MarcoLents/HaLO", "absolute_pre_study")
absolute_test
Absolute legibility ratings on a 5-point Likert scale (0 = very legible, 4 = not legible at all) from the test set. Four annotators independently rated samples from the test partition. Not all annotators rated every sample; missing values indicate unrated samples. Where a rater provided multiple ratings for the same sample, the value is the rounded mean.
Note: The
absolute_pre_studyandabsolute_testconfigs use different annotator numbering schemes because they originate from separate annotation campaigns described in different sections of the paper. Some of the underlying raters overlap between the two configs, but the pseudonyms are independent.
| Field | Description |
|---|---|
sampleId |
Sample ID |
referenceSentenceId |
Reference sentence ID |
annotator8 -- annotator11 |
Integer Likert ratings (0--4) per annotator, NaN if not rated |
samplePath |
Path to the feature .npy file |
Splits: train (391)
absolute_test = load_dataset("MarcoLents/HaLO", "absolute_test")
aspect_ratio
Image aspect ratio for each sample.
| Field | Description |
|---|---|
sampleId |
Sample ID |
aspectRatio |
Width-to-height ratio of the original image |
samplePath |
Path to the feature .npy file |
Splits: train (1,134), test (391), validation (382)
aspect_ratio = load_dataset("MarcoLents/HaLO", "aspect_ratio")
Feature Files
Pixtral 12B image embeddings are stored as .npy files under features/{referenceSentenceId}/{sampleId}.npy. Each file contains a 1024-dimensional float32 vector obtained by mean-pooling the token-level outputs of the frozen Pixtral-ViT encoder.
To download the feature files:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="MarcoLents/HaLO",
repo_type="dataset",
allow_patterns="features/*/*.npy",
local_dir="data"
)
Citation
If you use this dataset, please cite:
@article{bauer2025halo,
title={Ranking handwriting images like a human: AI-based legibility assessment by comparative ranking on the HaLO dataset},
author={Bauer, Meike and Lents, Marco and Schmidt, Erik and Hamann, Tim and Pieger, Lukas and Salata, Susanne and Di Salvo, Francesco and Hoffmann, Tal and Barth, Jens and Ledig, Christian},
year={2025}
}
License
This dataset is released under the MIT License.
Acknowledgements
This study was supported by the Hightech Agenda Bayern (HTA) of the Free State of Bavaria, Germany. Additional support was provided by the Bavarian State Ministry of Economic Affairs, Regional Development and Energy (StMWi) as part of the Bavarian Collaborative Research Programme (BayVFP), funding line Digitalization, project KIBEL, grant number DIK0813.