---
language:
- en
license: mit
task_categories:
- image-classification
tags:
- computer-vision
- sign-language
- neuromorphic
- event-based
- asl
- spiking-neural-networks
pretty_name: TCASL
size_categories:
- 10K<n<100K
---

# TCASL: Temporal Contrast ASL Dataset

## Overview

TCASL is an American Sign Language (ASL) image classification dataset generated using **temporal contrast emulation**, a software technique that mimics the behavior of neuromorphic Dynamic Vision Sensors (DVS). Rather than capturing standard RGB frames, each sample is a sparse, edge-based event map that isolates hand motion and discards static background noise.

The dataset was built to support real-time, low-power ASL finger-spelling recognition on consumer hardware without requiring specialized event cameras or high-end GPUs.

## Dataset Details

| Property | Value |
|---|---|
| Classes | 26 (A–Z) |
| Total Samples | 13,000 |
| Samples per Class | 500 |
| Resolution | 128 × 128 px (grayscale) |
| Format | Image classification (folder-per-class) |
| Participants | 5 |

## Splits

| Split | Samples |
|---|---|
| Train | 10,400 |
| Val | 1,300 |
| Test | 1,300 |

## Sample Images

Below is one example from each class, showing the sparse event-map representation produced by temporal contrast emulation. White pixels represent ON events (brightening), black pixels represent OFF events (darkening), and gray is the neutral background (no motion).

![Class samples](dataset.png)

## How It Works

Standard webcams produce full-color frames at fixed intervals. TCASL simulates a neuromorphic sensor by computing pixel-level brightness differences between consecutive frames. If the change exceeds a threshold θ, an event is recorded:

- **+1 (white)** — pixel got brighter (ON event)  
- **−1 (black)** — pixel got darker (OFF event)  
- **0 (gray)** — no significant change (ignored)

This eliminates redundant background data and retains only the moving hand contour, dramatically reducing the data a model needs to process.

## Data Collection

- **Participants:** 5 individuals with varying hand shapes and sizes
- **Recording:** Standard consumer webcam; temporal contrast emulation applied in post-processing
- **Volume:** 100 samples per participant × 26 classes = 13,000 total
- **Dynamic letters:** ASL letters J and Z involve motion. For consistency with static gestures, only the final hand position was captured
- **Quality control:** All samples were manually reviewed; blurry frames or incorrect gestures were discarded

## Usage

```python
from datasets import load_dataset

ds = load_dataset("keshavshankar08/TCASL")

# Access a split
train = ds["train"]
print(train[0])  # {"image": <PIL.Image>, "label": 0}

# Label index → letter mapping
label_names = train.features["label"].names  # ["a", "b", ..., "z"]
```

## Benchmark Results

The following table shows top-1 accuracy across architectures on the TCASL test set.

| Architecture | Accuracy |
|---|---|
| LeNet-5 | 82.3% |
| Hybrid Transformer | 92.5% |
| RG-CNN | 96.8% |
| SDNN (ours) | **98.3%** |
| STBP-SNN | 98.6% |

The custom SDNN achieves 98.3% accuracy and runs at over 200 FPS on a standard laptop CPU (Apple M1), with no GPU required.

## Motivation

Millions of people rely on sign language to communicate, yet real-time translation tools typically require expensive hardware or high-end GPUs. TCASL addresses this by bringing neuromorphic vision to standard webcams through software emulation, enabling accessible, privacy-preserving ASL recognition at the edge.

## Related Work

This dataset was created alongside the **TCASL Learner**, a real-time "Spelling Bee" game application that runs finger-spelling recognition entirely on a consumer laptop. For full details on the architecture, training paradigm, and experimental results, see the [GitHub Page](https://github.com/keshavshankar08/TCASL).

## Citation

```bibtex
@misc{tcasl2026,
  title={TCASL: Real-Time American Sign Language Classification via Temporal Contrast Emulation},
  author={Keshav Shankar and Nathaniel Ginck},
  year={2026},
  note={Technical Report},
  url={https://github.com/keshavshankar08/TCASL}
}
```

## License

This dataset is released under the [MIT License](LICENSE).