Real time Hand Gesture Recognition using different algorithms based on American Sign Language

Islam, Md. Mohiminul; Siddiqua, Sarah; Afnan, Jawata

doi:10.1109/icivpr.2017.7890854

Cited by 84 publications

(25 citation statements)

References 3 publications

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“…In the next steps, these signals are analysed with the help of a Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) trained with Connectionist Temporal Classification (CTC). Islam et al [10] proposed a novel "K convex hull" method which is the combination of K curvature and convex hull algorithms. The "K convex hull" method developed is able to detect fingertip with high accuracy.…”

Section: Estrela Et Al (2013)mentioning

confidence: 99%

“…The first one is electromagnetic gloves and sensors based detection and the other is Computer Vision based. The electromagnetic gloves and sensor based technique is very expensive and is not suitable for real life purposes [10]. On the other hand, the Computer Vision based technique can be further divided into Static Gesture Recognition and Dynamic Gesture Recognition.…”

Section: Introductionmentioning

confidence: 99%

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Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks

Khari¹,

Garg²,

Crespo³

et al. 2019

IJIMAI

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In this era, the interaction between Human and Computers has always been a fascinating field. With the rapid development in the field of Computer Vision, gesture based recognition systems have always been an interesting and diverse topic. Though recognizing human gestures in the form of sign language is a very complex and challenging task. Recently various traditional methods were used for performing sign language recognition but achieving high accuracy is still a challenging task. This paper proposes a RGB and RGB-D static gesture recognition method by using a fine-tuned VGG19 model. The fine-tuned VGG19 model uses a feature concatenate layer of RGB and RGB-D images for increasing the accuracy of the neural network. Finally, on an American Sign Language (ASL) Recognition dataset, the authors implemented the proposed model. The authors achieved 94.8% recognition rate and compared the model with other CNN and traditional algorithms on the same dataset.

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Section: Estrela Et Al (2013)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks

Khari¹,

Garg²,

Crespo³

et al. 2019

IJIMAI

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“…Accuracy achieved by the proposed system is 97% on 24 gestures. Further ED has been also used with convex-hull for extracting features to improve accuracy and make system more reliable [44]. Several distinct features like eccentricity, fingertip finder, elongatedness, rotation and pixel segmentation are used for feature extraction.…”

Section: Cbirmentioning

confidence: 99%

Feature Extraction Technique for Vision-Based Indian Sign Language Recognition System: A Review

Tyagi

Bansal

2020

Advances in Intelligent Systems and Computing

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Vision-based sign language recognition system is an emerging research area in human-computer interaction (HCI). It has been proved as a powerful communication tool for deaf and mute society, irrespective of geographical differences. Automatic sign language recognition system works broadly in three phases namely image pre-processing, feature extraction and classification of gestures. The output of the feature extraction phase is crucial for various classifiers. This paper presents a comprehensive review of feature extraction techniques used in vision-based sign language recognition system. A taxonomy of currently used techniques for feature extraction has been presented. The paper concludes by presenting future direction in feature extraction technique for Indian sign language (ISL).

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“…La interacción hombre-máquina (HCI) es un campo de investigación basado en la interacción humana con computadoras o máquinas. El reconocimiento de gestos manuales (HGR) pasa a ser un subcampo de HCI que se incluye en la presente investigación, el reconocimiento de gestos de mano utilizando algoritmos que permitan representar un sistema de HGR en tiempo real está siendo trabajado por muchos investigadores a nivel mundial, se puede citar el sistema de señas americano (ASL) que utiliza conjuntamente el algoritmo de detección de aristas y el algoritmo de detección de piel para obtener mejores resultados [27], mientras que, el sistema de señas Indio (ISL) es digitalizado mediante el reconocimiento basado en los momentos Krawtchouk y dual-Hahn como la mejor opción a otros métodos recientemente propuestos [28], la aplicación desarrollada utiliza el algoritmo k-nearest neighbour algorithm (W-KNN) para lograr la clasificación y reconocimiento en tiempo real de los gestos de la mano [29] y que una vez que se realiza el lenguaje de señas frente a la cámara del dispositivo se logre su traducción tanto al español como al kichwa.…”

Section: Desarrollo Del Modelo Generalunclassified

Recognition, interpretation and teaching of sign lan-guage in the official languages of Ecuador

Duque¹,

Naula²,

Reina³

et al. 2018

KEG

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Abstract. The lack of an application that can be used within the teaching sign language learning process allowed us to propose this research, creating two technological applications based on the modalities and materials used during the Sign Language teaching process in Ecuador, using its own methodology divided into four phases, it was obtained that the desktop application recognizes and interprets in real time the gestures made with a range of effectiveness of 90% to 100% Working on clear funds, while the APP allows you to learn the basic signs and words used by people with hearing impairment, both applications work in Spanish and Kichwa.

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Real time Hand Gesture Recognition using different algorithms based on American Sign Language

Cited by 84 publications

References 3 publications

Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks

Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks

Feature Extraction Technique for Vision-Based Indian Sign Language Recognition System: A Review

Recognition, interpretation and teaching of sign lan-guage in the official languages of Ecuador

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