https://github.com/mazensayed91/asl_recognition

https://github.com/mazensayed91/asl_recognition

Last synced: 11 months ago
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• Host: GitHub
• URL: https://github.com/mazensayed91/asl_recognition
• Owner: Mazensayed91
• Created: 2020-11-08T18:13:00.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2020-11-08T18:19:36.000Z (over 5 years ago)
• Last Synced: 2025-04-04T08:47:35.671Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 12.2 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## American Sign Language (ASL)

American Sign Language (ASL) is the primary language used by many deaf individuals in North America, and it is also used by hard-of-hearing and hearing individuals.  The language is as rich as spoken languages and employs signs made with the hand, along with facial gestures and bodily postures.


A lot of recent progress has been made towards developing computer vision systems that translate sign language to spoken language.  This technology  often relies on complex neural network architectures that can detect subtle patterns in streaming video.  However, as a first step, towards understanding how to build a translation system, we can reduce the size of the problem by translating individual letters, instead of sentences.

In this notebook, we will train a convolutional neural network to classify images of American Sign Language (ASL) letters.  After loading, examining, and preprocessing the data, we will train the network and test its performance.

In the code cell below, we load the training and test data. 





x_train and x_test are arrays of image data with shape (num_samples, 3, 50, 50), corresponding to the training and test datasets, respectively.



y_train and y_test are arrays of category labels with shape (num_samples,), corresponding to the training and test datasets, respectively.