Thai sign language recognition by using geometric invariant feature and ANN classification
Thai sign language recognition by using geometric invariant feature and ANN classification
复制标题
使用几何不变特征和ANN分类的泰语手语识别
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
C. Pintavirooj
中科院分区：
文献类型：
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作者：
Suchin Adhan;C. Pintavirooj
手语是听障或聋人的主要交流工具。人们可以使用它来进行有效的交流，但挑战是与计算机进行交流。人机交互(HCI)将对它们的使用产生积极影响。因此，本文将手势作为人机交互中的一个重要研究领域。本研究致力于利用进化几何不变特征进行二维图像识别，并开发了一种两层前馈神经网络来识别和翻译泰语手语(TSL)字母表中的42个字母的手势。我们设计了有六种不同颜色记号笔的手套供实验使用。结果表明，该系统能够识别42个TSL字母，平均准确率为96.19%。
Hand sign language is the primary communication tool for people with hearing-impaired or deaf. People can use it to communicate effectively but the challenge is to communicate with the computer. Human computer interaction (HCI) will have a positive impact on their use. Thus, this is to bring the hand gestures in HCI as an important research area. This research focuses on 2D image recognition utilizing an evolved geometric invariant feature and also have developed a two-layer feedforward neural network to identify and translate hand gesture pose of the 42 letters in the Thai Sign Language (TSL) alphabet to Thai alphabets. We designed glove with six different colored markers for using in the experiment. The result shows that this system is able to recognize 42 TSL alphabets with an average accuracy of 96.19 %.