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首页> 外文会议>Iberian Conference on Information Systems and Technologies >Recognition of hand configuration: A critical factor in automatic sign language translation
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Recognition of hand configuration: A critical factor in automatic sign language translation

机译：手势识别：自动手语翻译的关键因素

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Identifying hand configuration is a critical feature of sign language translation. In this paper, we describe our approach to recognize hand configurations in real time with the purpose of providing accurate predictions to be used in automatic sign language translation. To capture the hand configuration we rely on data gloves with 14 sensors that measure finger joints bending. These inputs are sampled at a frequency of 100Hz and fed to a classifier that predicts the current hand configuration. The classification model is created from an annotated sample of hand configurations previously acquired. We expect this approach to be accurate and robust in the sense that the performance of the classification model should not vary significantly when the classifier is being used by one or another user. The results from our experimental evaluation show that there is a very high accuracy, meaning that data gloves are a good approach to capture the descriptive features of hand configurations. However, the robustness of such an approach is not as good as desirable since the accuracy of the classifier depends on the user, i.e., the accuracy is high when the classifier is used by a user who trained it but decreases in other cases.
机译：识别手的配置是手语翻译的关键功能。在本文中，我们描述了一种实时识别手形的方法，目的是提供准确的预测以用于自动手语翻译。为了捕获手的形状，我们依靠带有14个传感器的数据手套来测量手指关节的弯曲度。这些输入以100Hz的频率采样，并馈入预测当前指针配置的分类器。分类模型是从先前获取的手形注释文档中创建的。在一个或另一个用户使用分类器时，分类模型的性能不应有显着变化，在这种意义上，我们希望这种方法是准确且健壮的。我们的实验评估结果表明，该方法具有很高的准确性，这意味着数据手套是捕获手形特征的一种好方法。但是，由于分类器的准确性取决于用户，因此这种方法的鲁棒性不如所期望的，即，当分类器由训练过该分类器的用户使用时，准确性很高，而在其他情况下会降低。

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