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ASL Finger Spelling Recognition using LSTM

This project aims to recognize finger spelling from hand poses presented as RGB images.

model.lua provides a sample run of the model on a pre-selected sequence of training and testing images chosen for the purpose of this demonstration. With everything else in place, this file can be run as simply as "th model.lua"

Image Localization

We use Google Incpetion architecture to achieve this. The main library used here is TensorFlow

Depth Estimation

We use a residual network for this. This is implemented in TensorFlow again.

Demo Run

th model.lua

This will do a sample run for a chunked data set consisting of a sequence length of 5. Make sure to unzip the hand_images.zip file before running this command.