Indian sign language recognition using graph matching on 3D motion captured signs

Kumar, D. Anil; Sastry, Ananth; Kishore, P. V. V.; Kumar, E. Kiran

doi:10.1007/s11042-018-6199-7

Cited by 29 publications

(9 citation statements)

References 45 publications

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“…Özbay and Safar used the Hausdorff distance and Hu invariants to process hand movements in a universal sign language recognition system [27]. Since the depth camera is able to convert human body images into human joint information, the meanings of signs are embedded in the distribution of joints [28]. Kishore et al proposed a characterization of sign language gestures articulated at different body parts as 3D motionlets, which describe the signs with a subset of joint motions [29].…”

Section: Related Workmentioning

confidence: 99%

Deep Forest-Based Monocular Visual Sign Language Recognition

Xue

Wang

et al. 2019

Applied Sciences

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Sign language recognition (SLR) is a bridge linking the hearing impaired and the general public. Some SLR methods using wearable data gloves are not portable enough to provide daily sign language translation service, while visual SLR is more flexible to work with in most scenes. This paper introduces a monocular vision-based approach to SLR. Human skeleton action recognition is proposed to express semantic information, including the representation of signs’ gestures, using the regularization of body joint features and a deep-forest-based semantic classifier with a voting strategy. We test our approach on the public American Sign Language Lexicon Video Dataset (ASLLVD) and a private testing set. It proves to achieve a promising performance and shows a high generalization capability on the testing set.

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Section: Related Workmentioning

confidence: 99%

Deep Forest-Based Monocular Visual Sign Language Recognition

Xue

Wang

et al. 2019

Applied Sciences

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“…various sign is made using fingers and palm of the hand which can be categorized into 26 alphabetical characters of English language. However, in this project we have used the American standard of sign language there are other ways also for sign language in Chinese and Indian standard for sign recognition for their native languages such as Mandarin and Devanagari [6], [9]. It is most usually utilized by hard of hearing and moronic individuals who have hearing or discourse issues to convey among themselves or with typical individuals.…”

Section: Recent Studiesmentioning

confidence: 99%

SNCHAR : Sign language Character Recognition

Chaurasia*¹,

Kshire²

2019

IJRTE

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Hard of hearing and unable to speak individuals convey among themselves utilizing gesture based communication yet typical individuals think that it’s hard to comprehend their language. Utilizing two hands frequently prompts lack of definition of highlights because of covering of hands. Our undertaking goes for making the essential stride in crossing over the correspondence hole between typical individuals and tragically challenged individuals utilizing Sign language. Powerful augmentation of this undertaking to words and typical statements may not just cause the deaf and dumb individuals to impart quicker and simpler with external world, yet in addition give a lift in creating self-sufficient frameworks for comprehension and supporting them. Gesture based communication is the favored technique for correspondence among the hard of hearing and the meeting debilitated individuals everywhere throughout the world. Acknowledgment of communication via gestures can have shifting level of achievement when utilized in a computer vision or some other techniques. Communication via gestures is said to have an organized arrangement of signals where each motion is having a particular significance. We propose a solution to this problem as SNCHAR will allow easy interaction between the deaf and the hearing impaired people and the ones who are not. Here SN stands for Sign language, CHA for Character, and R for Recognition system. The project "SNCHAR: Sign language Character Recognition" system is a python based application. It uses live video as input, and predicts the letters the user is gesturing in the live feed. It captures the frames, and recognizes the area of hand gesture by looking for skin color intensity object. It separates the gesture area from the rest of the frame, and feeds that part to our pre-trained model. This pre-trained model, using the hand gesture as input predicts a value that represents an alphabet. This alphabet is displayed on the screen. User can hear the text predicted on the screen by pressing “P” on the keyboard. The predicted text can be erased if required by using “Z” from the keyboard

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“…Applications in the area of computer vision include optical character recognition [61,79], biometric identification [44,22] and medical diagnostics [86], and 3D object recognition [15]. As a sample in the past year graphs have been used to recognize Indian sign language [57,56], spot subgraphs (e.g., certain characters) in comic book images [58], and to stack MRI image slices [18]. A more comprehensive timeline can be found in [20].…”

Section: Pattern Recognitionmentioning

confidence: 99%

An interdisciplinary survey of network similarity methods

Evans,

Graham

2019

Preprint

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Indian sign language recognition using graph matching on 3D motion captured signs

Cited by 29 publications

References 45 publications

Deep Forest-Based Monocular Visual Sign Language Recognition

Deep Forest-Based Monocular Visual Sign Language Recognition

SNCHAR : Sign language Character Recognition

An interdisciplinary survey of network similarity methods

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