Of late, the research world has been vigorously involved in, inventing strategy and techniques to improve the spontaneity of Human Computer Interaction (HCI). Gesture recognition is one of the most probable techniques in this area. The eventual aim here is to introduce an intelligent system for hand gesture recognition in both static and dynamic area, which is still a challenging point due to the lag of valuable beneficial methods. The main intent of this paper is to implement an efficient hand gesture recognition model considering both static and dynamic datasets for Indian Sign Languages (ISL). In static type, images are taken for processing, whereas video frames are used for processing the dynamic type. The proposed recognition model involves five main steps “(a) Image pre-processing, (b) gesture segmentation, (c) Feature extraction, (d) Optimal Feature Selection, and (e) Recognition”. In the pre-processing phase, greyscale conversion and histogram equalization are performed. The pre-processed image is subjected to the segmentation process, where the Active Contour model and Canny Edge Detection is implemented. In the feature extraction phase, both the contour image, and the edge detected image is deployed, in which Histogram of Oriented Gradients (HOG) features are extracted from the contour image, and Edge Oriented Histogram (EOH) features are extracted from edge detected images. To reduce the dimension of HOG, and EOH features, Principle Component Analysis (PCA) is applied. Further, the region props features are extracted for both contour and edge detected image. Finally, all these features are summed, and the optimal feature selection process performs here to select the unique feature giving different information with less correlation. Finally, the recognition classifier called Neural Network (NN) is adopted, where the new training algorithm is used to update network weight. Dynamic Time Warping (DTW) method helps to remove the repeated frames in the video and to reduce the time consumption of testing. In both feature selection and classification, a hybrid algorithm Deer Hunting-based Grey Wolf Optimization (DH-GWO) is used for selecting the features and weight update in NN as well. Hence, the integration of a hybrid meta-heuristic algorithm is highly efficient for recognizing the characters for images and words for videos with high recognition accuracy.

最近，研究界积极参与了发明提高人机交互（HCI）自发性的策略和技术。手势识别是该领域最可能的技术之一。这里的最终目的是在静态和动态区域中引入用于手势识别的智能系统，由于有价值的有益方法的滞后，这仍然是一个挑战点。本文的主要目的是在考虑印度手语（ISL）静态和动态数据集的情况下，实现一种有效的手势识别模型。在静态类型中，将拍摄图像进行处理，而视频帧则用于处理动态类型。拟议的识别模型包括五个主要步骤：“（a）图像预处理，（b）手势分割，（c）特征提取，（d）最佳特征选择，和（e）识别”。在预处理阶段，执行灰度转换和直方图均衡化。预处理的图像经过分割处理，在其中实现了主动轮廓模型和Canny Edge Detection。在特征提取阶段，部署轮廓图像和边缘检测图像，从轮廓图像中提取定向梯度直方图（HOG）特征，并从边缘检测图像中提取边缘定向直方图（EOH）特征。为了减小HOG和EOH功能的尺寸，应用了主成分分析（PCA）。此外，针对轮廓和边缘检测图像两者提取区域道具特征。最后，总结所有这些功能，最佳特征选择过程在此执行以选择具有较少相关性的不同信息的独特特征。最后，采用了称为神经网络（NN）的识别分类器，其中使用了新的训练算法来更新网络权重。动态时间规整（DTW）方法有助于去除视频中的重复帧并减少测试的时间消耗。在特征选择和分类中，还使用基于Deer Hunting的混合算法灰狼优化（DH-GWO）在NN中选择特征和权重更新。因此，混合元启发式算法的集成对于以高识别精度识别图像的字符和视频的单词非常有效。采用了称为神经网络（NN）的识别分类器，其中使用了新的训练算法来更新网络权重。动态时间规整（DTW）方法有助于去除视频中的重复帧并减少测试的时间消耗。在特征选择和分类中，还使用基于Deer Hunting的混合算法灰狼优化（DH-GWO）在NN中选择特征和权重更新。因此，混合元启发式算法的集成对于以高识别精度识别图像的字符和视频的单词非常有效。采用了称为神经网络（NN）的识别分类器，其中使用了新的训练算法来更新网络权重。动态时间规整（DTW）方法有助于去除视频中的重复帧并减少测试的时间消耗。在特征选择和分类中，还使用基于Deer Hunting的混合算法灰狼优化（DH-GWO）在NN中选择特征和权重更新。因此，混合元启发式算法的集成对于以高识别精度识别图像的字符和视频的单词非常有效。动态时间规整（DTW）方法有助于去除视频中的重复帧并减少测试的时间消耗。在特征选择和分类中，还使用基于Deer Hunting的混合算法灰狼优化（DH-GWO）在NN中选择特征和权重更新。因此，混合元启发式算法的集成对于以高识别精度识别图像的字符和视频的单词非常有效。动态时间规整（DTW）方法有助于去除视频中的重复帧并减少测试的时间消耗。在特征选择和分类中，还使用基于Deer Hunting的混合算法灰狼优化（DH-GWO）在NN中选择特征和权重更新。因此，混合元启发式算法的集成对于以高识别精度识别图像的字符和视频的单词非常有效。