Android Based Portable Hand Sign Recognition System

Raheja, Jagdish Lal; Singhal, Ananya; Chaudhary, Ankit

doi:10.15579/gcsr.vol3.ch1

Cited by 18 publications

(10 citation statements)

References 16 publications

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“…The training system is based on Microsoft Windows platform and x86 processor. Gesture recognition for mobile platforms is developed in [13], but gesture modeling on mobile devices is not performed.…”

Section: Methodsmentioning

confidence: 99%

Dactyl Alphabet Modeling and Recognition Using Cross Platform Software

Kondratiuk

Krak

2018

2018 IEEE Second International Conference on Data Stream Mining &Amp; Processing (DSMP)

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The technology, which is implemented with cross platform tools, is proposed for modeling of gesture units of sign language, animation between states of gesture units with a combination of gestures (words). Implemented technology simulates sequence of gestures using virtual spatial hand model and performs recognition of dactyl items from camera input. With the cross platform means technology achieves the ability to run on multiple platforms without re-implementing for each platform.

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Section: Methodsmentioning

confidence: 99%

Dactyl Alphabet Modeling and Recognition Using Cross Platform Software

Kondratiuk

Krak

2018

2018 IEEE Second International Conference on Data Stream Mining &Amp; Processing (DSMP)

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“…In particular, the use of hands and gestures is a very attractive method for providing natural human-computer interaction (Erol et al, 2007). Control interfaces based on gesture have been developed based on both static hand postures and dynamic motion patterns (Raheja et al, 2015;Chaudhary, 2017). Indeed, hands represent a control device with a high degree of freedom, very useful to manipulate complex machinery (Raheja et al, 2010;Wu et al, 2010) or to train systems such as surgical simulations (Badash et al, 2016).…”

Section: Background and Motivationmentioning

confidence: 99%

A Database for Learning Numbers by Visual Finger Recognition in Developmental Neuro-Robotics

et al. 2021

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Numerical cognition is a fundamental component of human intelligence that has not been fully understood yet. Indeed, it is a subject of research in many disciplines, e.g., neuroscience, education, cognitive and developmental psychology, philosophy of mathematics, linguistics. In Artificial Intelligence, aspects of numerical cognition have been modelled through neural networks to replicate and analytically study children behaviours. However, artificial models need to incorporate realistic sensory-motor information from the body to fully mimic the children's learning behaviours, e.g., the use of fingers to learn and manipulate numbers. To this end, this article presents a database of images, focused on number representation with fingers using both human and robot hands, which can constitute the base for building new realistic models of numerical cognition in humanoid robots, enabling a grounded learning approach in developmental autonomous agents. The article provides a benchmark analysis of the datasets in the database that are used to train, validate, and test five state-of-the art deep neural networks, which are compared for classification accuracy together with an analysis of the computational requirements of each network. The discussion highlights the trade-off between speed and precision in the detection, which is required for realistic applications in robotics.

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“…The images are mostly colorful. Other colors are expressed generally through the mutual overlap of red, green and blue [10]. If the color is ignored in the recognition process, grey processing shall be carried out to change the color value into black and white, which is generally expressed with the gray value.…”

Section: ) Grey Processingmentioning

confidence: 99%

Design of FPGA-based Handwriting Image Recognition System

Wang¹,

Yang²,

Xu³

et al. 2017

AMA_B

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This paper designs a program to realize the handwriting image recognition algorithm using FPGA. The digital character recognition system designed in this paper is composed of software design and hardware design. In terms of software, the character processing is divided into image denoising, image binaryzation, grey processing and refinement treatment. Each process algorithm is developed with Matlab. By virtue of its powerful image processing function, the algorithm is simulated. By transforming the DBN into large data volume matrix operations and combining the implementability of FPGA, each link of the identification algorithm is determined. The program mainly uses Altera Cyclone IV chip and several calculation cores connected by DBN are realized through hardware programming. The hardware implementation of the module function and algorithm is verified through Modelsim simulation of each module.

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Android Based Portable Hand Sign Recognition System

Cited by 18 publications

References 16 publications

Dactyl Alphabet Modeling and Recognition Using Cross Platform Software

Dactyl Alphabet Modeling and Recognition Using Cross Platform Software

A Database for Learning Numbers by Visual Finger Recognition in Developmental Neuro-Robotics

Design of FPGA-based Handwriting Image Recognition System

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