---
license: cc-by-nc-sa-4.0
task_categories:
- video-classification
- feature-extraction
language:
- en
tags:
- asl
- sign-language
- keypoints
- mediapipe
- isolated-sign-recognition
- asl-citizen
size_categories:
- 10K<n<100K
---

# ASL Citizen Processed Keypoints-200

This dataset was processed from the ASL Citizen keypoints-200 Kaggle dataset.

## Purpose

This processed version is prepared for isolated American Sign Language recognition and encoder pretraining.

It is intended to be used as the first stage of a larger ASL-to-English pipeline:

1. Train isolated sign encoder on ASL Citizen.
2. Transfer the encoder into a CTC model.
3. Fine-tune on a continuous ASL dataset such as How2Sign.
4. Decode continuous signing into gloss/text.

## Feature Format

Original PKL samples:

```text
(T, 75, 4)
```

Where:

- T = number of frames
- 75 = selected landmarks/keypoints
- 4 = values per keypoint

Processed NumPy samples:

```text
(num_samples, sequence_length, feature_dim)
```

## Included Files

- train_features.npy
- train_labels.npy
- val_features.npy
- val_labels.npy
- test_features.npy
- test_labels.npy
- label_map.json
- id_to_label.json
- class_mapping.json
- metadata.json

## Labels

Labels are stored as integer class IDs.

Use id_to_label.json to convert predicted class IDs back into readable ASL sign labels.

## Loading Example

```python
import numpy as np
import json

train_features = np.load("train_features.npy")
train_labels = np.load("train_labels.npy")

with open("id_to_label.json", "r", encoding="utf-8") as f:
    id_to_label = json.load(f)

print("Train features:", train_features.shape)
print("Train labels:", train_labels.shape)
print("First label:", id_to_label[str(train_labels[0])])
```

## Notes

This dataset contains keypoint-based features, not raw videos.

The goal is to reduce training cost and make the dataset easier to use for sign-language recognition models.

## Citation

This dataset is derived from ASL Citizen-related data. Please cite the original ASL Citizen dataset/source when using this processed version.