Real time hand gesture recognition system for android devices

Lahiani, Houssem; Elleuch, M.; Kherallah, Monji

doi:10.1109/isda.2015.7489184

Cited by 46 publications

(10 citation statements)

References 8 publications

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“…Most importantly, reviewing these studies can direct any interested researchers toward the most relevant research that may contain further information regarding a topic of interest. The sample size represents a combination of the total number of gestures that were displayed during the experimental evaluation and the number of classes to which a particular sign could belong [179], [180]. A larger sample size indicates more reliable results, and is always preferable.…”

Section: Related Studiesmentioning

confidence: 99%

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Al‐Qurishi¹,

Khalid²,

Souissi³

2021

IEEE Access

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People with hearing impairments are found worldwide; therefore, the development of effective local level sign language recognition (SLR) tools is essential. We conducted a comprehensive review of automated sign language recognition based on machine/deep learning methods and techniques published between 2014 and 2021 and concluded that the current methods require conceptual classification to interpret all available data correctly. Thus, we turned our attention to elements that are common to almost all sign language recognition methodologies. This paper discusses their relative strengths and weaknesses, and we propose a general framework for researchers. This study also indicates that input modalities bear great significance in this field; it appears that recognition based on a combination of data sources, including vision-based and sensor-based channels, is superior to a unimodal analysis. In addition, recent advances have allowed researchers to move from simple recognition of sign language characters and words towards the capacity to translate continuous sign language communication with minimal delay. Many of the presented models are relatively effective for a range of tasks, but none currently possess the necessary generalization potential for commercial deployment. However, the pace of research is encouraging, and further progress is expected if specific difficulties are resolved.

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Section: Related Studiesmentioning

confidence: 99%

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Al‐Qurishi¹,

Khalid²,

Souissi³

2021

IEEE Access

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“…This histogram is fed to decision tree classifier. In [15], from the segmented hand image, hand contour was obtained, which was then used for fitting a convex hull and convexity defects were found out. Using this, the fingers were identified and the angles between the adjacent ones were determined.…”

Section: Related Workmentioning

confidence: 99%

Real-time Indian Sign Language (ISL) Recognition

Shenoy

Dastane

Rao

et al. 2018

2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

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This paper presents a system which can recognise hand poses & gestures from the Indian Sign Language (ISL) in real-time using grid-based features. This system attempts to bridge the communication gap between the hearing and speech impaired and the rest of the society. The existing solutions either provide relatively low accuracy or do not work in real-time. This system provides good results on both the parameters. It can identify 33 hand poses and some gestures from the ISL. Sign Language is captured from a smartphone camera and its frames are transmitted to a remote server for processing. The use of any external hardware (such as gloves or the Microsoft Kinect sensor) is avoided, making it user-friendly. Techniques such as Face detection, Object stabilisation and Skin Colour Segmentation are used for hand detection and tracking. The image is further subjected to a Grid-based Feature Extraction technique which represents the hand's pose in the form of a Feature Vector. Hand poses are then classified using the k-Nearest Neighbours algorithm. On the other hand, for gesture classification, the motion and intermediate hand poses observation sequences are fed to Hidden Markov Model chains corresponding to the 12 pre-selected gestures defined in ISL.Using this methodology, the system is able to achieve an accuracy of 99.7% for static hand poses, and an accuracy of 97.23% for gesture recognition.

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“…And this module makes the aggregate performance of the DSLR system as accurate as 98.65%. Houssem Lahiani et al [2] proposed a system based on SVM for recognizing various hand gesture. The system consist of four steps: hand segmentation, smoothing, feature extraction & classification.…”

Section: Literature Outlinementioning

confidence: 99%

Mobile Application Based Translation of Sign Language to Text Description in Kannada Language

Kagalkar

Gumaste²

2018

Int. J. Interact. Mob. Technol.

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Abstract-Sign language is a main mode of communication for vocally disabled. This language use set of representation which is finger sign, expression or mixture of both to express their information among others. This system presents a novel approach for mobile application based translation of sign action analysis, recognition and generating a text description in Kannada language. Where it uses two important steps training and testing. In training set of 50 different domains of video samples are collected, each domain contains 5 samples and assign a class of words to each video sample and it will be store in database. Where in testing test sample under goes preprocessing using median filter, canny operator for edge detection, HOG for feature extraction. SVM takes input as a HOG features and predict the class label based on trained SVM model. Finally the text description will be generated in Kannada language. The average computation time is minimum and with acceptable recognition rate and validate the performance efficiency over the conventional model.

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Real time hand gesture recognition system for android devices

Cited by 46 publications

References 8 publications

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Real-time Indian Sign Language (ISL) Recognition

Mobile Application Based Translation of Sign Language to Text Description in Kannada Language

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