---
license: apache-2.0
tags:
  - tensorflow-lite
  - edge-ai
  - asl-recognition
  - mediapipe
  - computer-vision
  - gesture-recognition
library_name: tensorflow
inference: false
datasets: []
model-index:
  - name: ASL-TFLite-Edge
    results: []
---

# ASL-TFLite-Edge

This repository contains a TensorFlow Lite model trained to recognize American Sign Language (ASL) fingerspelling gestures using hand landmark data. The model is optimized for real-time inference on edge devices.

## 🧠 Model Details

- **Format:** TensorFlow Lite (.tflite)
- **Input:** 64x64 RGB image (generated from hand landmarks via Mediapipe)
- **Output:** Softmax probabilities over 59 ASL character classes (including a padding token)
- **Frameworks:** TensorFlow, Mediapipe

## 📁 Files Included

- `asl_model.tflite` – The TFLite model file for ASL recognition
- `inference_args.json` – JSON file containing the selected columns used for inference from parquet data
- `tflite_inference.py` – Inference script to run predictions from raw `.parquet` landmark files

## 🚀 How to Run Inference
You can download and load the TFLite model directly from Hugging Face using the `huggingface_hub` library.

### Clone the image
```bash
git lfs install
git clone https://huggingface.co/ColdSlim/ASL-TFLite-Edge
cd ASL-TFLite-Edge
```
### Requirements
```bash
pip install -r requirements.txt
```

### Run the Script
```bash
python tflite_inference.py path/to/sample.parquet
```

### Output
```bash
Predicted class index: 5
```
>🔁 You can map this class index back to the character using your `char_to_num` mapping used during training.

## 📌 Example Workflow
1. Extract right-hand landmark data from Mediapipe and store it in a `.parquet` file.

2. Ensure it contains the same selected_columns as in `inference_args.json`.

3. Run `tflite_inference.py` to get the predicted class.

## 🧾 License
This project is licensed under the Apache 2.0 License.

## 👨‍💻 Author
Developed by Manik Sheokand

For sign language fingerspelling Recognition on edge devices using TensorFlow Lite