ABSTRACT Sign language is a medium of communication for people with hearing disabilities. Static and dynamic gestures are identified in a video-based sign language recognition and translated them into humanly understandable phrases to achieve the communication objective. However, videos contain redundant Key-frames which require additional processing. Number of such Key-frames can be reduced. The selection of particular Key-frames without losing the required information is a challenging task. The Key-frame extraction algorithm is used which helps to speed-up the sign language recognition process by extracting essential key-frames. The proposed framework eliminates the computation overhead by picking up the distinct Key-frames for the recognition process. Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Histograms of Oriented Gradient (HOG) are used for unique features extraction. We used the bagged tree, boosted tree ensemble method, Fine KNN, and SVM for classification. We tested methodology on video-based datasets of Pakistani Sign Language. It achieved an overall 97.5% accuracy on 37 Urdu alphabets and 95.6% accuracy on 100 common words.

中文翻译：

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一种新的基于关键帧选择的视频数据手语识别框架

摘要 手语是听力障碍人士的交流媒介。在基于视频的手语识别中识别静态和动态手势，并将它们翻译成人类可以理解的短语，以实现交流目标。但是，视频包含需要额外处理的冗余关键帧。可以减少这种关键帧的数量。在不丢失所需信息的情况下选择特定的关键帧是一项具有挑战性的任务。使用关键帧提取算法，通过提取必要的关键帧来帮助加快手语识别过程。所提出的框架通过为识别过程选取不同的关键帧来消除计算开销。离散小波变换 (DWT)、离散余弦变换 (DCT)、和定向梯度直方图 (HOG) 用于独特特征提取。我们使用袋装树、增强树集成方法、Fine KNN 和 SVM 进行分类。我们在基于视频的巴基斯坦手语数据集上测试了方法。它在 37 个乌尔都语字母表上的总体准确率达到了 97.5%，在 100 个常用词上的准确率达到了 95.6%。