Continuous sign language recognition (SLR) is a challenging task that
requires learning on both spatial and temporal dimensions of signing frame
sequences. Most recent work accomplishes this by using CNN and RNN hybrid
networks. However, training these networks is generally non-trivial, and most
of them fail in learning unseen sequence patterns, causing an unsatisfactory
performance for online recognition. In this paper, we propose a fully
convolutional network (FCN) for online SLR to concurrently learn spatial and
temporal features from weakly annotated video sequences with only
sentence-level annotations given. A gloss feature enhancement (GFE) module is
introduced in the proposed network to enforce better sequence alignment
learning. The proposed network is end-to-end trainable without any
pre-training. We conduct experiments on two large scale SLR datasets.
Experiments show that our method for continuous SLR is effective and performs
well in online recognition.

本文提出了使用全卷积网络 (FCN) 实现在线手语识别的方法，同时从仅具有句子级注释的弱注释视频序列中学习空间和时间特征，通过引入一个手语特征增强 (GFE) 模块来提高序列对齐学习的效果，实验表明该方法在连续手语识别上有效且在在线识别中表现出了较好的性能。