Add 107 new samples (20260119_203234_zmem)

Appended 107 handwriting samples in Parquet and JSONL format for RNN training

README.md

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+---
+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
+---
+
+# handwriting-v1
+
+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<double> | Delta X offsets between consecutive points |
+| `dy` | list<double> | Delta Y offsets between consecutive points |
+| `eos` | list<double> | 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:
+
+```json
+{"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)
+
+```python
+from datasets import load_dataset
+
+# For private repos, use: load_dataset("finnbusse/handwriting-v1", token="YOUR_HF_TOKEN")
+dataset = load_dataset("finnbusse/handwriting-v1")
+
+# 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`
+
+```python
+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.

data/20260119_203234_zmem.parquet

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