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American Sign Language recognition is a difficult gesture recognitionproblem, characterized by fast, highly articulate gestures. These are comprisedof arm movements with different hand shapes, facial expression and headmovements. Among these components, hand shape is the vital, often the mostdiscriminative part of a gesture. In this work, we present an approach foreffective learning of hand shape embeddings, which are discriminative for ASLgestures. For hand shape recognition our method uses a mix of manually labelledhand shapes and high confidence predictions to train deep convolutional neuralnetwork (CNN). The sequential gesture component is captured by recursive neuralnetwork (RNN) trained on the embeddings learned in the first stage. We willdemonstrate that higher quality hand shape models can significantly improve theaccuracy of final video gesture classification in challenging conditions withvariety of speakers, different illumination and significant motion blurr. Wecompare our model to alternative approaches exploiting different modalities andrepresentations of the data and show improved video gesture recognitionaccuracy on GMU-ASL51 benchmark dataset

FineHand: Learning Hand Shapes for American Sign Language Recognition