Sign Language Recognition using Machine Learning

Deaf and dumb people communicate with others and within their own groups by using sign language. Beginning with the acquisition of sign gestures, computer recognition of sign language continues until text or speech is produced. There are two types of sign gestures: static and dynamic. Both gesture recognition systems, though static gesture recognition is easier to use than dynamic gesture recognition, are crucial to the human race. In this survey, the steps for sign language recognition are detailed. Examined are the data collection, preprocessing, transformation, feature extraction, classification, and outcomes. There were also some recommendations for furthering this field of study

An Analysis of Machine- and Human-Analytics in Classification

In this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that amy be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the "bag of features" approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory. Both found that the visual analytics approach had some advantages over the machine learning approach, especially when sparse datasets were used as the ground truth. We examine various possible factors that may have contributed to such advantages, and collect empirical evidence for supporting the observation and reasoning of these factors. We propose an information-theoretic model as a common theoretic basis to explain the phenomena exhibited in these two case studies. Together we provide interconnected empirical and theoretical evidence to support the usefulness of visual analytics

DIDACTIC SUPPORT FOR THE FORMATION OF SELF-CONTROL SKILLS IN FOREIGN TEXTBOOKS: ALTERNATIVE APPROACHES

The paper studies didactic approaches used to create self-test units in textbooks. The system of self-control skills formation is considered on the example of textbooks on reading. The authors define the approaches of textbooks published in Kyrgyzstan, Kazakhstan, Russia, the Netherlands, Germany, France and Greece in Cyrillic, Latin and Greek alphabets to the organization of self-control based on the content and structure. The didactic features of these publications are described from the viewpoint of realizing the possibility of self-test of the tasks and exercises. German and Greek textbooks with an original system of self-test in illustrative and textual form are singled out and analyzed in detail. The types of tasks for the thinking development in schoolchildren are distinguished with the use of cluster analysis. The content analysis helped the authors in identifying the five groups of multiple or single selection of objects and things tasks and exercises, reproduction of previously studied letters, sequencing, correlation, and design. The paper shows the similarities and differences in the implementation of self-control skills formation in various editions of textbooks. The separate unit of exercises for the development of fine motor skills are considered as a means of developing graphic accuracy and a prerequisite for the transition to the stage of logical thinking

Aerospace medicine and biology. A continuing bibliography with indexes, supplement 195

This bibliography lists 148 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1979

Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts

This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize. The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution. Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead

Enabling sign language instruction with technology: the case of developing a computerized learning tool for Ghanaian Sign Language (GhSL)

Thesis submitted to the Department of Business Administration, Ashesi University College, in partial fulfillment of Bachelor of Science degree in Business Administration, April 2012Ghanaian Sign Language (GhSL) is a developing language in the sense that not much is known about it within or outside Ghana. The fact that GhSL is closely-related to American Sign Language (ASL) means that, knowledge of ASL can help one communicate with the Ghanaian Deaf. However, there exist Ghanaian-specific signs that are not available in ASL. This paper presents the Ghanaian Interactive Sign Language (GISL) Tutor, the first computer-based tutor for GhSL designed to teach GhSL vocabulary of Ghanaian-specific signs. Ghanaians who tested the tutor during its iteration stage expressed that they want more Ghanaian signs to be available on the tutor. The purpose of the GISL Tutor is therefore to make Ghanaian-specific signs accessible to anyone interested in learning GhSL for reception and expression

Hand gesture spotting and recognition using HMMs and CRFs in color image sequences

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010von Mahmoud Othman Selim Mahmoud Elmezai

ENABLING TECHNIQUES FOR EXPRESSIVE FLOW FIELD VISUALIZATION AND EXPLORATION

Flow visualization plays an important role in many scientific and engineering disciplines such as climate modeling, turbulent combustion, and automobile design. The most common method for flow visualization is to display integral flow lines such as streamlines computed from particle tracing. Effective streamline visualization should capture flow patterns and display them with appropriate density, so that critical flow information can be visually acquired. In this dissertation, we present several approaches that facilitate expressive flow field visualization and exploration. First, we design a unified information-theoretic framework to model streamline selection and viewpoint selection as symmetric problems. Two interrelated information channels are constructed between a pool of candidate streamlines and a set of sample viewpoints. Based on these information channels, we define streamline information and viewpoint information to select best streamlines and viewpoints, respectively. Second, we present a focus+context framework to magnify small features and reduce occlusion around them while compacting the context region in a full view. This framework parititions the volume into blocks and deforms them to guide streamline repositioning. The desired deformation is formulated into energy terms and achieved by minimizing the energy function. Third, measuring the similarity of integral curves is fundamental to many tasks such as feature detection, pattern querying, streamline clustering and hierarchical exploration. We introduce FlowString that extracts shape invariant features from streamlines to form an alphabet of characters, and encodes each streamline into a string. The similarity of two streamline segments then becomes a specially designed edit distance between two strings. Leveraging the suffix tree, FlowString provides a string-based method for exploratory streamline analysis and visualization. A universal alphabet is learned from multiple data sets to capture basic flow patterns that exist in a variety of flow fields. This allows easy comparison and efficient query across data sets. Fourth, for exploration of vascular data sets, which contain a series of vector fields together with multiple scalar fields, we design a web-based approach for users to investigate the relationship among different properties guided by histograms. The vessel structure is mapped from the 3D volume space to a 2D graph, which allow more efficient interaction and effective visualization on websites. A segmentation scheme is proposed to divide the vessel structure based on a user specified property to further explore the distribution of that property over space