| --- |
| 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). |
|
|
|  |
|
|
| ## 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). |
