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Simple sign language alphabet recognizer using Python, openCV and tensorflow for training Inception model (CNN classifier).
| Date | Stars |
|---|---|
| 2026-07-24 | 323 |
| 2026-07-25 | 323 |
| 2026-07-28 | 323 |
| 2026-07-30 | 323 |
| 2026-08-06 | 323 |
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# SLR Alphabet Recognizer This project is a sign language alphabet recognizer using Python, openCV and tensorflow for training InceptionV3 model, a convolutional neural network model for classification. The framework used for the CNN implementation can be found here: [Simple transfer learning with an Inception V3 architecture model](https://github.com/xuetsing/image-classification-tensorflow) by xuetsing The project contains the dataset (1Go). If you are only interested in code, you better copy/paste the few files than cloning the entire project. You can [find the demo here](https://youtu.be/kBw-xGEIYhY) [](http://www.youtube.com/watch?v=kBw-xGEIYhY) ## Requirements This project uses python 3.5 and the PIP following packages: * opencv * tensorflow * matplotlib * numpy See requirements.txt and Dockerfile for versions and required APT packages ### Using Docker ``` docker build -t hands-classifier . docker run -it hands-classifier bash ``` ### Install using PIP ``` pip3 install -r requirements.txt ``` ## Training To train the model, use the following command (see framework github link for more command options): ``` python3 train.py \ --bottleneck_dir=logs/bottlenecks \ --how_many_training_steps=2000 \ --model_dir=inception \ --summaries_dir=logs/training_summaries/basic \ --output_graph=logs/trained_graph.pb \ --output_labels=logs/trained_labels.txt \ --image_dir=./dataset ``` If you're using the provided dataset, it may take up to three hours. ## Classifying To test classification, use the following command: ``` python3 classify.py path/to/image.jpg ``` ## Using webcam (demo) To use webcam, use the following command: ``` python3 classify_webcam.py ``` Your hand must be inside the rectangle. Keep position to write word, see demo for deletions.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c5206ad919e49d5b, topic:deep-learning, topic:tensorflow
matched fp:c5206ad919e49d5b, topic:computer-vision, readme:image classification