Using Deep Convolutional Networks for Gesture Recognition in American Sign Language

Vivek, Bheda,; Radpour, Dianna

doi:10.48550/arxiv.1710.06836

Cited by 12 publications

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“…At this end of the curve, the observed performance was comparable/similar to that reported in e.g. [7] (see also references therein). The numbers of errors per each gesture in the trained system are shown in Table 2.…”

Section: Experiments and Resultssupporting

confidence: 88%

Fast construction of correcting ensembles for legacy Artificial Intelligence systems: Algorithms and a case study

Tyukin

Gorban

Green

et al. 2019

Information Sciences

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This paper presents a new approach for constructing simple and computationally efficient improvements of generic Artificial Intelligence (AI) systems, including Multilayer and Deep Learning neural networks. The improvements are small network ensembles added to the existing AI architectures. Theoretical foundations of the approach are based on stochastic separation theorems and the ideas of the concentration of measure. We show that, subject to mild technical assumptions on statistical properties of internal signals in the original AI system, the approach enables fast removal of the AI's errors with probability close to one on the datasets which may be exponentially large in dimension. The approach is illustrated with numerical examples and a case study of digits recognition in American Sign Language.

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Section: Experiments and Resultssupporting

confidence: 88%

Fast construction of correcting ensembles for legacy Artificial Intelligence systems: Algorithms and a case study

Tyukin

Gorban

Green

et al. 2019

Information Sciences

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“…With this approach, Pigou et al [8] achieved an accuracy of 91.7%. Bheda et al [10] used a deep convolutional network to classify ASL with alphabets and digits. However, unlike the previous two approaches, this work used cascaded CNN rather than the traditional CNN.…”

Section: Custom Cnn Modelsmentioning

confidence: 99%

A Light-Weight Deep Learning-Based Architecture for Sign Language Classification

Nareshkumar¹,

Jaison²

2023

Intelligent Automation &Amp; Soft Computing

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With advancements in computing powers and the overall quality of images captured on everyday cameras, a much wider range of possibilities has opened in various scenarios. This fact has several implications for deaf and dumb people as they have a chance to communicate with a greater number of people much easier. More than ever before, there is a plethora of info about sign language usage in the real world. Sign languages, and by extension the datasets available, are of two forms, isolated sign language and continuous sign language. The main difference between the two types is that in isolated sign language, the hand signs cover individual letters of the alphabet. In continuous sign language, entire words' hand signs are used. This paper will explore a novel deep learning architecture that will use recently published large pre-trained image models to quickly and accurately recognize the alphabets in the American Sign Language (ASL). The study will focus on isolated sign language to demonstrate that it is possible to achieve a high level of classification accuracy on the data, thereby showing that interpreters can be implemented in the real world. The newly proposed Mobile-NetV2 architecture serves as the backbone of this study. It is designed to run on end devices like mobile phones and infer signals (what does it infer) from images in a relatively short amount of time. With the proposed architecture in this paper, the classification accuracy of 98.77% in the Indian Sign Language (ISL) and American Sign Language (ASL) is achieved, outperforming the existing state-of-the-art systems.

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“…CNNs have also been applied in order to recognize sign language in a single frame [39] or a sequence of frames [3] (dynamic gestures). In [40], the CNN takes both intensity and depth video sequences as input for the recognition of dynamic gestures with the objective of designing touchless interfaces in cars.…”

Section: Related Workmentioning

confidence: 99%

Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks

Alashhab,

Gallego,

Lozano

2022

Preprint

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This paper proposes an interactive system for mobile devices controlled by hand gestures aimed at helping people with visual impairments. This system allows the user to interact with the device by making simple static and dynamic hand gestures. Each gesture triggers a different action in the system, such as object recognition, scene description or image scaling (e.g., pointing a finger at an object will show a description of it). The system is based on a multi-head neural network architecture, which initially detects and classifies the gestures, and subsequently, depending on the gesture detected, performs a second stage that carries out the corresponding action. This multi-head architecture optimizes the resources required to perform different tasks simultaneously, and takes advantage of the information obtained from an initial backbone to perform different processes in a second stage. To train and evaluate the system, a dataset with about 40k images was manually compiled and labeled including different types of hand gestures, backgrounds (indoors and outdoors), lighting conditions, etc. This dataset contains synthetic gestures (whose objective is to pre-train the system in order to improve the results) and real images captured using different mobile phones. The results obtained and the comparison made with the state of the art show competitive results as regards the different actions performed by the system, such as the accuracy of classification and localization of gestures, or the generation of descriptions for objects and scenes.

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Using Deep Convolutional Networks for Gesture Recognition in American Sign Language

Cited by 12 publications

References 0 publications

Fast construction of correcting ensembles for legacy Artificial Intelligence systems: Algorithms and a case study

Fast construction of correcting ensembles for legacy Artificial Intelligence systems: Algorithms and a case study

A Light-Weight Deep Learning-Based Architecture for Sign Language Classification

Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks

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