American Sign Language Recognizer Implementation. Project for the Udacity AI Nanodegree program.
The objective of this project is to identify American Sign Language (ASL) words and phrases from a pre-recorded database. Hidden Markov Models (HMMs) and n-gram language models are used to recognize words based on a collection of features extracted from the data set. The dataset can be found in the asl_recognizer/data/ directory and was derived from
the RWTH-BOSTON-104 Database.
The handpositions (hand_condensed.csv) are pulled directly from the database boston104.handpositions.rybach-forster-dreuw-2009-09-25.full.xml.The exctracted features used as HMM input include the position of the speaker's hands relative to the speaker's nose and the change in the speaker's hand locations frame-to-frame.
The project requires Python 3 and the following libraries:
To start the project run:
jupyter notebook asl_recognizer.ipynb