Datasets:

finnbusse
/

v2testing

license: mit
task_categories:
  - text-generation
language:
  - de
tags:
  - handwriting
  - stroke-data
  - rnn-training
  - stylus
  - s-pen
  - parquet
  - jsonl
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.parquet

v2testing

This dataset contains handwriting stroke data collected using a stylus (S Pen) on a tablet device. Optimized for training RNNs (Recurrent Neural Networks) on handwriting generation/recognition tasks.

Dataset Description

Schema Version: 1.0.0
Format: Apache Parquet (columnar, compressed) + JSONL backup
Language: German

Data Format

Data is available in two formats in the data/ directory:

Parquet files (*.parquet): Columnar format, optimized for HuggingFace datasets
JSONL files (*.jsonl): Line-delimited JSON backup, easy to parse

Both formats contain identical RNN training data with the same batch IDs.

Parquet Schema

Each row in the Parquet files represents a complete handwriting sample:

Column	Type	Description
`id`	string	Unique identifier (UUID)
`text`	string	The prompt text that was written
`dx`	list	Delta X offsets between consecutive points
`dy`	list	Delta Y offsets between consecutive points
`eos`	list	End-of-stroke flags (1 = pen lift, 0 = continue)
`scale`	double	Scale factor used for normalization
`created_at`	string	ISO timestamp of creation
`session_id`	string	Collection session identifier

JSONL Format

Each line in the JSONL files is a JSON object with the following structure:

{"id": "uuid", "text": "prompt text", "points": [{"dx": 0, "dy": 0, "eos": 0}, ...], "scale": 1.0}

Field	Type	Description
`id`	string	Unique identifier (UUID)
`text`	string	The prompt text that was written
`points`	array	Array of point objects with dx, dy, eos
`scale`	number (optional)	Scale factor used for normalization

RNN Training Format

The stroke data is stored in the format commonly used for RNN handwriting models:

dx/dy: Position deltas from the previous point (first point has dx=dy=0)
eos: Binary flag indicating pen lifts (end of stroke)
Data is normalized by bounding box for consistent scale

Visualization

Preview SVGs are available in renders_preview/ for HuggingFace Dataset Viewer.

Usage

Using Parquet (Recommended for HuggingFace)

from datasets import load_dataset

# For private repos, use: load_dataset("finnbusse/v2testing", token="YOUR_HF_TOKEN")
dataset = load_dataset("finnbusse/v2testing")

# Access a sample
sample = dataset['train'][0]

# Stroke data is already native Python lists (no JSON parsing needed)
dx = sample['dx']
dy = sample['dy']
eos = sample['eos']

# Reconstruct absolute positions
x, y = 0, 0
positions = []
for dx_i, dy_i, eos_i in zip(dx, dy, eos):
    x += dx_i
    y += dy_i
    positions.append((x, y, eos_i))

Using JSONL (Alternative)

JSONL filenames follow the batch ID pattern: YYYYMMDD_HHMMSS_XXXX.jsonl

import json
import glob

# Read all JSONL files in the data directory
for jsonl_file in glob.glob('data/*.jsonl'):
    with open(jsonl_file, 'r') as f:
        for line in f:
            sample = json.loads(line)
            points = sample['points']
            scale = sample.get('scale', 1.0)  # scale is optional
            # Each point has: dx, dy, eos

Collection Method

Data was collected using a web application with Pointer Events API, capturing stylus input including pressure and tilt when available.