论文标题
使用孤立的标志和后处理中的连续手语中的单词分离
Word separation in continuous sign language using isolated signs and post-processing
论文作者
论文摘要
。连续的手语识别（CSLR）是计算机视觉中的一项艰巨任务，因为在符号句子中检测单词之间的明确边界的困难。为了应对这一挑战，我们提出了一个两阶段的模型。在第一阶段，对CNN，SVD和LSTM组合的预测模型进行了训练。在第二阶段，我们将一种后处理算法应用于从模型第一部分获得的软磁输出，以便在连续符号中分离隔离符号。虽然提出的模型在具有相似帧数的隔离标志类上进行了培训，但在连续的符号视频上对其进行评估，每个隔离符号类别都具有不同的帧长度。由于缺乏大型数据集，包括符号序列和相应的孤立标志，因此使用了两个孤立的手语识别（ISLR），RKS-Persiansign和asllvd的公共数据集进行评估。连续符号视频的结果证实了所提出的模型处理孤立符号边界检测的效率。
. Continuous Sign Language Recognition (CSLR) is a long challenging task in Computer Vision due to the difficulties in detecting the explicit boundaries between the words in a sign sentence. To deal with this challenge, we propose a two-stage model. In the first stage, the predictor model, which includes a combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the second stage, we apply a post-processing algorithm to the Softmax outputs obtained from the first part of the model in order to separate the isolated signs in the continuous signs. While the proposed model is trained on the isolated sign classes with similar frame numbers, it is evaluated on the continuous sign videos with a different frame length per each isolated sign class. Due to the lack of a large dataset, including both the sign sequences and the corresponding isolated signs, two public datasets in Isolated Sign Language Recognition (ISLR), RKS-PERSIANSIGN and ASLLVD, are used for evaluation. Results of the continuous sign videos confirm the efficiency of the proposed model to deal with isolated sign boundaries detection.
