Architectures for Real-Time Automatic Sign Language Recognition on Resource-Constrained Device

Powerful, handheld computing devices have proliferated among consumers in recent years. Combined with new cameras and sensors capable of detecting objects in three-dimensional space, new gesture-based paradigms of human computer interaction are becoming available. One possible application of these developments is an automated sign language recognition system. This thesis reviews the existing body of work regarding computer recognition of sign language gestures as well as the design of systems for speech recognition, a similar problem. Little work has been done to apply the well-known architectural patterns of speech recognition systems to the domain of sign language recognition. This work creates a functional prototype of such a system, applying three architectures seen in speech recognition systems, using a Hidden Markov classifier with 75-90% accuracy. A thorough search of the literature indicates that no cloud-based system has yet been created for sign language recognition and this is the first implementation of its kind. Accordingly, there have been no empirical performance analyses regarding a cloud-based Automatic Sign Language Recognition (ASLR) system, which this research provides. The performance impact of each architecture, as well as the data interchange format, is then measured based on response time, CPU, memory, and network usage across an increasing vocabulary of sign language gestures. The results discussed herein suggest that a partially-offloaded client-server architecture, where feature extraction occurs on the client device and classification occurs in the cloud, is the ideal selection for all but the smallest vocabularies. Additionally, the results indicate that for the potentially large data sets transmitted for 3D gesture classification, a fast binary interchange protocol such as Protobuf has vastly superior performance to a text-based protocol such as JSON

Dynamic gesture recognition using PCA with multi-scale theory and HMM

In this paper, a dynamic gesture recognition system is presented which requires no special hardware other than a Webcam. The system is based on a novel method combining Principal Component Analysis (PCA) with hierarchical multi-scale theory and Discrete Hidden Markov Models (DHMM). We use a hierarchical decision tree based on multiscale theory. Firstly we convolve all members of the training data with a Gaussian kernel, which blurs differences between images and reduces their separation in feature space. This reduces the number of eigenvectors needed to describe the data. A principal component space is computed from the convolved data. We divide the data in this space into two clusters using the k-means algorithm. Then the level of blurring is reduced and PCA is applied to each of the clusters separately. A new principal component space is formed from each cluster. Each of these spaces is then divided into two and the process is repeated. We thus produce a binary tree of principal component spaces where each level of the tree represents a different degree of blurring. The search time is then proportional to the depth of the tree, which makes it possible to search hundreds of gestures in real time. The output of the decision tree is then input into DHMM to recognize temporal information

Linguistically-driven framework for computationally efficient and scalable sign recognition

We introduce a new general framework for sign recognition from monocular video using limited quantities of annotated data. The novelty of the hybrid framework we describe here is that we exploit state-of-the art learning methods while also incorporating features based on what we know about the linguistic composition of lexical signs. In particular, we analyze hand shape, orientation, location, and motion trajectories, and then use CRFs to combine this linguistically significant information for purposes of sign recognition. Our robust modeling and recognition of these sub-components of sign production allow an efficient parameterization of the sign recognition problem as compared with purely data-driven methods. This parameterization enables a scalable and extendable time-series learning approach that advances the state of the art in sign recognition, as shown by the results reported here for recognition of isolated, citation-form, lexical signs from American Sign Language (ASL)

Real time hand gesture recognition including hand segmentation and tracking

In this paper we present a system that performs automatic gesture recognition. The system consists of two main components: (i) A unified technique for segmentation and tracking of face and hands using a skin detection algorithm along with handling occlusion between skin objects to keep track of the status of the occluded parts. This is realized by combining 3 useful features, namely, color, motion and position. (ii) A static and dynamic gesture recognition system. Static gesture recognition is achieved using a robust hand shape classification, based on PCA subspaces, that is invariant to scale along with small translation and rotation transformations. Combining hand shape classification with position information and using DHMMs allows us to accomplish dynamic gesture recognition

#Socialtagging: Defining its Role in the Academic Library

The information environment is rapidly changing, affecting the ways in which information is organized and accessed. User needs and expectations have also changed due to the overwhelming influence of Web 2.0 tools. Conventional information systems no longer support evolving user needs. Based on current research, we explore a method that integrates the structure of controlled languages with the flexibility and adaptability of social tagging. This article discusses the current research and usage of social tagging and Web 2.0 applications within the academic library. Types of tags, the semiotics of tagging and its influence on indexing are covered