Indian Sign Language Interpreter with Android Implementation

N, Shanmukha Swamy M; Chethan, M P; Gatwadi, Mahantesh

doi:10.5120/17067-7484

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…P. Kishore et al [18] proposed a system by finding active contours from boundary edge map using Artificial Neural Network (ANN) to classify the signs. Another approach used the Viola Jones algorithm with LBP functions for hand gesture recognition in a real-time environment [19]. It had the advantage of requiring less processing capacity to detect the movements.…”

Section: IImentioning

confidence: 99%

Indian Sign Language Recognition Using CNN Inception V3 - A Review

Jagtap,

Pagare,

Sandbhor

et al. 2024

IJRASET

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This research paper explores the intersection of sign language recognition & deep learning, with a focus on the utilization of CNNs and the Inception V3 architecture. The study emphasizes how crucial proper translation and identification of sign language is to closing the communication gap that exists between the hearing and the Deaf populations. It discusses challenges such as limited datasets and ambiguity in sign language and outlines the potential for future advancements in accessibility and education. By combining the power of deep learning and culturally tailored datasets, this research paper paves the way for more effective sign language recognition, offering the promise of improved communication and accessibility for individuals with hearing impairments. It emphasizes how important it is to remove obstacles to communication and promote inclusion in all facets of daily life for the community of people who are Deaf and hard of hearing

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Section: IImentioning

confidence: 99%

Indian Sign Language Recognition Using CNN Inception V3 - A Review

Jagtap,

Pagare,

Sandbhor

et al. 2024

IJRASET

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“…Traditional appearance-based approach utilizes hand-crafted features generated by applying manually defined filters on images, such as HOG, 5,6 SIFT, 7,8 and LBP. 9,10 The approaches that utilize deep learning generate appearance-based features by learning the image filters, such as CNN and its variants. 11,12 The pose-based approaches estimate the pose of the human body by localizing key points (spatial coordinates) of joints in the human body (knees, elbows, wrists, and fingers) and use this pose information for extracting the features.…”

Section: Related Workmentioning

confidence: 99%

“…( 1) and ( 2) can be written as Eqs. ( 5)- (10) for updating the position of search agents, which are nonleader wolves: E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 5 ; 1 1 6 ; 6 8 7…”

Section: Improved Competitive Gray Wolf Optimizermentioning

confidence: 99%

“…These works fall into two groups based on the methods applied for extracting spatial features: appearance-based and pose-based. Traditional appearance-based approach utilizes hand-crafted features generated by applying manually defined filters on images, such as HOG, 5 , 6 SIFT, 7 , 8 and LBP 9 , 10 . The approaches that utilize deep learning generate appearance-based features by learning the image filters, such as CNN and its variants 11 , 12 .…”

Section: Related Workmentioning

confidence: 99%

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Optimization of convolutional neural network hyperparameters using improved competitive gray wolf optimizer for recognition of static signs of Indian Sign Language

2023

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In the last decade, the success achieved by convolutional neural networks (CNN) has changed the trend of research in the computer vision field from manual features to learned features for various applications. However, designing the architecture of CNN and selecting optimum values of its hyperparameters poses a critical challenge because of their massive and complex search space. Automatically picking up the optimal values of hyperparameters may facilitate the designers of CNN-based deep learning models. Swarm intelligence (SI)-based improved competitive gray wolf optimizer (ICGWO) in continuous search space is proposed for optimizing hyperparameters of custom-designed CNN architecture. To evaluate the performance of the ICGWO, we employ 16 benchmark functions. We also assess the effectiveness of the ICGWO by evaluating it with six state-of-art meta-heuristic algorithms. The experiment results show that the ICGWO can obtain the best accuracy on eleven benchmark functions and second best on two benchmark functions. The optimized CNN recognizes static signs of English alphabets in Indian Sign Language (ISL). We also evaluated the optimized CNN on the publicly available ISL dataset for the English alphabet, where it attains 99.93% validation accuracy. In order to evaluate the efficacy of the proposed variant of CGWO, we optimized the hyperparameters of CNN using the existing SI-based search optimization algorithms namely particle swarm optimization, whale optimization algorithm (WOA), differential evolution, and gray wolf optimizer as well.

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“…The resistance values of 4.5-inch flex sensors range from 7K to 15K and for 2.2-inch flex sensor, it ranges from 20K to 40K, as shorter the radius the more resistor value. Another 2.5K ohm resistor is utilized to build a voltage divider circuit with Vcc supply being 3.3volts taken from Arduino nano processor, the voltage values from the voltage divider circuit being analog in nature are given to the Arduino nano processor which has an inbuilt ADC [12]. Further, the IMU senses the hand movements and gives the digital values in XYZ direction called the roll, yaw, pitch respectively.…”

Section: Robotics and Automation Engineering Journalmentioning

confidence: 99%

Wireless Glove for Hand Gesture Acknowledgment: Sign Language to Discourse Change Framework in Territorial Dialect

Javed¹

2018

RAEJ

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Generally deaf-dumb people use sign language for communication, but they find it difficult to communicate in a society where most of the people do not understand sign language. Due to which communications between deaf-mute and a normal person have always been a challenging task. The idea proposed in this paper is a digital wireless glove which can convert sign language to text and speech output. The glove is embedded with flex sensors and a 9DOF inertial measurement unit (IMU) to recognize the gesture. A 9-degree of freedom inertial measurement unit (IMU) and flex sensors are used to track the orientation of fingers and motion of hand in three dimensional spaces which senses the gestures of a person in the form of bend of fingers and tilt of the hand fist. This system was tried for its practicality in changing over gesture based communication and gives the continuous discourse yield in local dialect and additionally shows the text on GLCD module. The text show being in English, the voice yield of the glove will be in provincial dialect (here Kannada). So this glove goes about as a communicator which in part encourages them to get their necessities and an interpreter giving greater adaptability in correspondence. In spite of the fact that the glove is planned for gesture based communication to discourse transformation, it is a multipurpose glove and discovers its applications in gaming, mechanical autonomy and therapeutic field.In this paper, we propose an approach to avoid the gap between customer and software robotics development. We define a EUD (End-User Development) environment based on the visual programming environment Scratch, which has already proven in children learning computer science. We explain the interests of the environment and show two examples based on the Lego Mindstorms and on the Robosoft Kompai robot.

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Indian Sign Language Interpreter with Android Implementation

Cited by 5 publications

References 8 publications

Indian Sign Language Recognition Using CNN Inception V3 - A Review

Indian Sign Language Recognition Using CNN Inception V3 - A Review

Optimization of convolutional neural network hyperparameters using improved competitive gray wolf optimizer for recognition of static signs of Indian Sign Language

Wireless Glove for Hand Gesture Acknowledgment: Sign Language to Discourse Change Framework in Territorial Dialect

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