| ---
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| license: apache-2.0
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| tags:
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| - tensorflow-lite
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| - edge-ai
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| - asl-recognition
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| - mediapipe
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| - computer-vision
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| - gesture-recognition
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| library_name: tensorflow
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| inference: false
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| datasets: []
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| model-index:
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| - name: ASL-TFLite-Edge
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| results: []
|
| ---
|
|
|
| # ASL-TFLite-Edge
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|
|
| 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.
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|
|
| ## 🧠 Model Details
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|
|
| - **Format:** TensorFlow Lite (.tflite)
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| - **Input:** 64x64 RGB image (generated from hand landmarks via Mediapipe)
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| - **Output:** Softmax probabilities over 59 ASL character classes (including a padding token)
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| - **Frameworks:** TensorFlow, Mediapipe
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|
|
| ## 📁 Files Included
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|
|
| - `asl_model.tflite` – The TFLite model file for ASL recognition
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| - `inference_args.json` – JSON file containing the selected columns used for inference from parquet data
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| - `tflite_inference.py` – Inference script to run predictions from raw `.parquet` landmark files
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|
|
| ## 🚀 How to Run Inference
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| You can download and load the TFLite model directly from Hugging Face using the `huggingface_hub` library.
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|
|
| ### Clone the image
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| ```bash
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| git lfs install
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| git clone https://huggingface.co/ColdSlim/ASL-TFLite-Edge
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| cd ASL-TFLite-Edge
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| ```
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| ### Requirements
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| ```bash
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| pip install -r requirements.txt
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| ```
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|
|
| ### Run the Script
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| ```bash
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| python tflite_inference.py path/to/sample.parquet
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| ```
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|
|
| ### Output
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| ```bash
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| Predicted class index: 5
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| ```
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| >🔁 You can map this class index back to the character using your `char_to_num` mapping used during training.
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|
|
| ## 📌 Example Workflow
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| 1. Extract right-hand landmark data from Mediapipe and store it in a `.parquet` file.
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|
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| 2. Ensure it contains the same selected_columns as in `inference_args.json`.
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|
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| 3. Run `tflite_inference.py` to get the predicted class.
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|
|
| ## 🧾 License
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| This project is licensed under the Apache 2.0 License.
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|
|
| ## 👨💻 Author
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| Developed by Manik Sheokand
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|
|
| For sign language fingerspelling Recognition on edge devices using TensorFlow Lite |
