Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation.

•We study the recognition of American Sign Language fingerspelling sequences from video.•We collect a new annotated multi-signer fingerspelling video data set.•We develop models for fingerspelling recognition with no lexicon constraints.•Our best models are segmental conditional random fields using deep neural network (DNN) features.•We achieve up to 92% signer-dependent letter accuracy, and 83% for multi-signer recognition with DNN adaptation.