Sign language recognition becomes a popular research field in human–computer interaction. Attention on hand signal analysis helps to make easy communication among computer and human for information sharing. Major focus of the gesture recognition system is to identify and recognise various gestures, by a computer. This study introduces optimisation of both classifier and feature set for static American sign language recognition. Initially, the hand part is segmented from other parts of the image through effective edge and skin colour detection. Thereafter, robust features are obtained using discrete cosine transform, Zernike moment, scale-invariant feature transform, speeded-up robust features, histogram of oriented gradients and binary object features from the segmented hand image. From these extracted features, an optimal feature set is selected by social ski driver optimisation algorithm. Deep Elman recurrent neural network classifier is then introduced for recognition purpose. Optimisation is performed on feature sets, derived by fusion of features obtained from the above methods, based on precision, accuracy, F -measure and recall. Finally, optimised feature set and best classifier are used to recognise the hand gesture for classification purpose. The performance of this proposed method is evaluated and compared with existing literature.

中文翻译：

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优化基于分类器和融合的特征集以实现静态美国手语识别

手语识别已成为人机交互领域的热门研究领域。注意手信号分析有助于使计算机和人之间轻松进行通信以共享信息。手势识别系统的主要焦点是通过计算机识别和识别各种手势。本研究介绍了针对静态美国手语识别的分类器和特征集的优化。最初，通过有效的边缘和肤色检测将手部从图像的其他部分中分割出来。此后，使用离散余弦变换，Zernike矩，尺度不变特征变换，加速的鲁棒特征，定向梯度直方图和来自分割手图像的二值对象特征来获得鲁棒特征。从这些提取的功能中，通过社交滑雪驾驶员优化算法选择最佳特征集。然后引入Deep Elman递归神经网络分类器以进行识别。根据精度，准确度，对通过上述方法获得的特征进行融合而得出的特征集进行优化F -测量和召回。最后，优化的特征集和最佳分类器用于识别手势以进行分类。对该方法的性能进行了评估，并与现有文献进行了比较。