A depth-based Indian Sign Language recognition using Microsoft Kinect

Raghuveera, T; Rajashekar, Deepthi; Mangalashri, R; Akshaya, R

doi:10.1007/s12046-019-1250-6

Cited by 71 publications

(15 citation statements)

References 14 publications

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“…To analyze the proposed algorithm, we performed the set of experiments with three databases such as hand gesture database for HCI [4,33], Sebastien Marcel Dynamic Hand Posture Database [34] and RMTH German finger spelling database [35]. The performance parameters of the system are analyzed on the basis of certain metrics such as sensitivity, specificity and accuracy [36,37]. The parameters are defined as follows.…”

Section: Resultsmentioning

confidence: 99%

Dense SIFT–Flow based Architecture for Recognizing Hand Gestures

Suni¹,

Gopakumar²

2020

Adv. sci. technol. eng. syst. j.

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Several challenges like changes in brightness, dynamic background, occlusion and inconsistency of camera position make the recognition of hand gestures difficult in any vision-based method. Diversity in finger shape, size, distribution and motion dynamics is also a big constraint. This leads to the motivation in developing a dense Scale Invariant Feature Transform (SIFT) flow based architecture for recognizing dynamic hand gestures. Initially, a combination of three frames differencing and skin filtering technique is used for hand detection to reduce the computational complexity followed by a SIFT flow technique to extract the features from the detected hand region. SIFT flow vectors obtained from every pixel can lead to overfilling, data redundancy and dimension disaster. A dual layer belief propagation algorithm is utilized to optimize the feature vectors to resolve the dimensionality problem. Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers are used to evaluate the performance of the developed framework. Experiments were conducted on hand gesture database for HCI, Sebastien Marcel Dynamic Hand Posture Database and RWTH German finger spelling database. The simulation results demonstrate that the developed architecture has excellent performance on the uneven background and varying camera position and it is robust against image noise. A comparative analysis with the state of the art methods illustrates the effectiveness of the architecture.

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Section: Resultsmentioning

confidence: 99%

Dense SIFT–Flow based Architecture for Recognizing Hand Gestures

Suni¹,

Gopakumar²

2020

Adv. sci. technol. eng. syst. j.

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“…The authors in ref. [7] used Microsoft Kinect Xbox 360 to capture ISL single handed, double handed finger spelling signs containing 140 unique gestures for a total of 4600 images. They have used Local Binary features, Robust features, and Histogram of Oriented Gradients (HOG) features for extracting valuable features which are required for classification.…”

Section: Related Workmentioning

confidence: 99%

Indian Sign Language Recognition Using Ensemble Based Classifier Combination

Sahoo

Sarangi

Yadav

2022

Macromolecular Symposia

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Indian Sign Language (ISL) is an alternate to written and direct communication language used in India and Indian subcontinent. It is commonly used by deaf and mute people who cannot speak and hear. The ISL is a new sign language as compared to other sign languages used in developed countries. There is a need for automatic recognition of any sign language including ISL due to its application aspects in current times. The automation of ISL will helpful to both communities (those who can interact only through ISL and those who do not know ISL at all), because in many places the unblessed people facing difficulty in communicating like at airports, railway stations, banks, hospitals, and other places. In this paper, the authors have tried to develop an automatic conversion from ISL signs to text in the domain of digits. The system requires a digital camera to capture the ISL signs and the underlying software will interpret the meaning of the image. To develop such a system, pattern recognition concept including image processing techniques have been used. Important features from the ISL signs are extracted using Histogram Oriented Gradient (HOG) technique. To classify the input images, two classification techniques have been used in the experiments namely Tree Bagger and Fit Ensemble.

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“…Joshi et al [37] designed a unimodal feature fusion that helps in minimising the feature vector size, as well as enhances performance for all the datasets but fails in recognizing the Indian Sign Language (ISL) complex background dataset. Raghuveera et al [38] proposed an ensemble method to recognize ISL singlehanded signs, double-handed signs and finger spelling signs of 4,600 images and got an accuracy of 71.85 %. Moreover, all these sensors have their advantages in terms of low cost and drawbacks related to motion data.…”

Section: Let Us Consider the Particulars And Of Vision And Sensor Basmentioning

confidence: 99%

Recognition of Russian and Indian sign languages used by the deaf people

Elakkiya¹,

Grif²,

Prikhodko³

et al. 2020

Science Bulletin of the NSTU

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In our paper, we consider approaches towards the recognition of sign languages used by the deaf people in Russia and India. The structure of the recognition system for individual gestures is proposed based on the identification of its five components: configuration, orientation, localization, movement and non-manual markers. We overview the methods applied for the recognition of both individual gestures and continuous Indian and Russian sign languages. In particular we consider the problem of building corpuses of sign languages, as well as sets of training data (datasets). We note the similarity of certain individual gestures in Russian and Indian sign languages and specify the structure of the local dataset for static gestures of the Russian sign language. For the dataset, 927 video files with static one-handed gestures were collected and converted to JSON using the OpenPose library. After analyzing 21 points of the skeletal model of the right hand, the obtained reliability for the choice of points equal to 0.61, which was found insufficient. It is noted that the recognition of individual gestures and sign speech in general is complicated by the need for accurate tracking of various components of the gestures, which are performed quite quickly and are complicated by overlapping hands and faces. To solve this problem, we further propose an approach related to the development of a biosimilar neural network, which is to process visual information similarly to the human cerebral cortex: identification of lines, construction of edges, detection of movements, identification of geometric shapes, determination of the direction and speed of the objects movement. We are currently testing a biologically similar neural network proposed by A.V. Kugaevskikh on video files from the Russian sign language dataset.

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A depth-based Indian Sign Language recognition using Microsoft Kinect

Cited by 71 publications

References 14 publications

Dense SIFT–Flow based Architecture for Recognizing Hand Gestures

Dense SIFT–Flow based Architecture for Recognizing Hand Gestures

Indian Sign Language Recognition Using Ensemble Based Classifier Combination

Recognition of Russian and Indian sign languages used by the deaf people

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