American and russian sign language dactyl recognition

Ilya Makarov, Nikolay Veldyaykin, M. Chertkov, Aleksei Pokoev
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引用次数: 13

Abstract

Sign languages are the main way for people from deaf community to communicate with other people. In this paper, we have compared several real-time sign language dactyl recognition systems using deep convolutional neural networks. Our system is able to recognize words from natural language gestured using signs for each letter. We evaluate our approach on American (ASL) and Russian (RSL) sign languages. For ASL, we trained on dataset prepared by Massey University, Institute of Information and Mathematical Sciences, for RSL we collect our own dataset, which we aim to enlarge together with RSL community in Russia. The results showed 100% accuracy for ASL Massey dataset, while RSL recognition quality is behind sufficient quality due to much more complex nature of real-world RSL dataset.
美国和俄罗斯手语的长短格识别
手语是聋哑人与他人交流的主要方式。在本文中,我们比较了几种使用深度卷积神经网络的实时手语指格识别系统。我们的系统能够从自然语言中识别每个字母的手势。我们评估我们的方法在美国(ASL)和俄罗斯(RSL)手语。对于ASL,我们使用梅西大学信息与数学科学研究所准备的数据集进行训练,对于RSL,我们收集自己的数据集,我们的目标是与俄罗斯的RSL社区一起扩大数据集。结果表明,ASL Massey数据集的识别准确率为100%,而RSL识别质量由于实际RSL数据集的复杂性而落后于足够的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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