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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Interactive real time fuzzy class level gesture similarity measure based sign language recognition using artificial neural networks
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Interactive real time fuzzy class level gesture similarity measure based sign language recognition using artificial neural networks

机译：交互式实时模糊类级别手势相似度基于人工神经网络的手语识别

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摘要

Vision-based Sign Language Recognition has been an open research problem since decades. Many existing methods for sign recognition works well under restricted laboratory conditions but failed to support real-time scenarios because extraction of manual and non-manual movements with constantly changing shapes of signs are considered as tedious problem in machine vision and machine learning. To overcome these shortcomings, an interactive real time class level gesture similarity based sign recognition using Artificial Neural Network is presented in this paper. The method uses the sign images and starts with enhancing the image quality. The quality enhancement is performed by equalizing the histograms of luminance and contrast. The features of hand as subunits from quality improved image have been extracted by template matching techniques. Extracted features are used to generate neural network and trained with different class of signs. The classification is performed by measuring the class level gesture similarity measure towards each class of signs and images. Based on the measure estimated, the method classifies the image and sign. The result produced to the user has been iterated based on the actions provided by the user. The method is capable of iterating the result and recognition till the user gets satisfied. The method produces higher accuracy in sign recognition and reduces the false ratio.
机译：基于视觉的行语识别是自十年以来的开放研究问题。许多现有的签署识别方法在受限制的实验室条件下运作良好，但不支持实时方案，因为在机器视觉和机器学习中，使用不断变化的迹象的手动和非手动运动的提取被视为乏味的问题。为了克服这些缺点，本文介绍了使用人工神经网络的交互式实时级别姿态相似性的标志识别。该方法使用标志图像并开始增强图像质量。通过均衡亮度和对比度的直方图来执行质量增强。通过模板匹配技术提取了来自质量改进图像的亚基的手的特征。提取的特征用于生成神经网络并用不同类别的符号培训。通过测量朝向每类标志和图像的类级手势相似度来执行分类。基于估计的措施，该方法对图像和标志进行分类。基于用户提供的操作迭代给用户生成的结果。该方法能够迭代结果和识别，直到用户满意。该方法在签署识别中产生更高的精度，并降低了假比。

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