The recent development of disability studies in academic bodies has expedited the promotion of investigation on disability. With computer-aided tools, communication between the impaired person and someone who does not understand sign language could be accessible. A large number of people across the world are using sign language (e.g., British Sign Language (BSL) , Asian Sign Language (ASL) , Indian Sign Language (ISL) , etc.) with hand gestures for communication. In BSL recognition, the involvement of both hands overlapping each other becomes the main challenge. Moreover, BSL comprises ambiguous signs concerning viewpoint. However, existing traditional techniques seem in-stable, less accurate, and inefficient. In this work, the BSL fingerspelling alphabet recognition problem explores using a Deep learning framework to address the above-mentioned concerns. Convolutional Neural Network (CNN) is employed to detect and recognize for classification of 26 alphabets after being trained on the BSL corpus dataset. The proposed work outperforms the existing works with better precision (6%), recall (4%), and F-measure (5̃%). It reported better results on the BSL corpus dataset and webcam videos. The model achieved better accuracy (98.0%) for a large lexicon of words than previous models (Goh & Holden [ 6 ]: 69.5%, Rambhau [ 9 ]: 79.2%, and Liwicki et al. [ 8 ]: 92.5%). The 3D CNN-based proposal performs robust hand detection, much more accurate sign recognition, more scalability, and less ambiguity in BSL finger-spelling recognition.

In the multimedia era a large volume of video data can be recorded during a certain period of time by multiple cameras. Such a rapid growth of video data requires both effective and efficient multiview video summarization techniques. The users can quickly browse and comprehend a large amount of audiovisual data. It is very difficult in real-time to manage and access the huge amount of video-content-handling issues of interview dependencies significant variations in illumination and presence of many unimportant frames with low activity. In this paper we propose a local-alignment-based FASTA approach to summarize the events in multiview videos as a solution of the aforementioned problems. A deep learning framework is used to extract the features to resolve the problem of variations in illumination and to remove fine texture details and detect the objects in a frame. Interview dependencies among multiple views of video are then captured via the FASTA algorithm through local alignment. Finally object tracking is applied to extract the frames with low activity. Subjective as well as objective evaluations clearly indicate the effectiveness of the proposed approach. Experiments show that the proposed summarization method successfully reduces the video content while keeping momentous information in the form of events. A computing analysis of the system also shows that it meets the requirement of real-time applications.