We present two solutions to sentence-level SLR. Sentence-level SLR required
mapping videos of sign language sentences to sequences of gloss labels.
Connectionist Temporal Classification (CTC) has been used as the classifier
level of both models. CTC is used to avoid pre-segmenting the sentences into
individual words. The first model is an LRCN-based model, and the second model
is a Multi-Cue Network. LRCN is a model in which a CNN as a feature extractor
is applied to each frame before feeding them into an LSTM. In the first
approach, no prior knowledge has been leveraged. Raw frames are fed into an
18-layer LRCN with a CTC on top. In the second approach, three main
characteristics (hand shape, hand position, and hand movement information)
associated with each sign have been extracted using Mediapipe. 2D landmarks of
hand shape have been used to create the skeleton of the hands and then are fed
to a CONV-LSTM model. Hand locations and hand positions as relative distance to
head are fed to separate LSTMs. All three sources of information have been then
integrated into a Multi-Cue network with a CTC classification layer. We
evaluated the performance of proposed models on RWTH-PHOENIX-Weather. After
performing an excessive search on model hyper-parameters such as the number of
feature maps, input size, batch size, sequence length, LSTM memory cell,
regularization, and dropout, we were able to achieve 35 Word Error Rate (WER).

本文提出了两种解决方案来解决句子级手语识别问题，并通过使用连接主义时间分类（CTC）作为两种模型的分类器层，介绍了 LRCN-based 模型和 Multi-Cue 网络的工作原理。在评估了 RWTH-PHOENIX-Weather 数据集后，作者通过进行超参数搜索，包括特征图数量、输入大小、批量大小、序列长度、LSTM 内存单元、正则化和失活等，获得了 35% 的字词错误率（WER）。