scholarly journals Non-Verbal (Sign Language) To Verbal Language Translator Using Convolutional Neural Network

2022 ◽  
Vol 10 (1) ◽  
pp. 269-273
Author(s):  
Poonam Yerpude
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Sign Language ◽  
The Body ◽  
Image Processing Technique ◽  
Hand Gestures ◽  
Verbal Language ◽  
Architecture Model ◽  
Indian Sign Language

Abstract: Communication is very imperative for daily life. Normal people use verbal language for communication while people with disabilities use sign language for communication. Sign language is a way of communicating by using the hand gestures and parts of the body instead of speaking and listening. As not all people are familiar with sign language, there lies a language barrier. There has been much research in this field to remove this barrier. There are mainly 2 ways in which we can convert the sign language into speech or text to close the gap, i.e. , Sensor based technique,and Image processing. In this paper we will have a look at the Image processing technique, for which we will be using the Convolutional Neural Network (CNN). So, we have built a sign detector, which will recognise the sign numbers from 1 to 10. It can be easily extended to recognise other hand gestures including alphabets (A- Z) and expressions. We are creating this model based on Indian Sign Language(ISL). Keywords: Multi Level Perceptron (MLP), Convolutional Neural Network (CNN), Indian Sign Language(ISL), Region of interest(ROI), Artificial Neural Network(ANN), VGG 16(CNN vision architecture model), SGD(Stochastic Gradient Descent).

Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition

2019 ◽  
Vol 10 (3) ◽  
pp. 60-73 ◽  
Author(s):  
Ravinder Ahuja ◽  
Daksh Jain ◽  
Deepanshu Sachdeva ◽  
Archit Garg ◽  
Chirag Rajput
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
American Sign Language ◽  
Sign Language ◽  
Automated System ◽  
World Health ◽  
American Sign ◽  
Hand Gestures ◽  
The World ◽  
Health Organization

Communicating through hand gestures with each other is simply called the language of signs. It is an acceptable language for communication among deaf and dumb people in this society. The society of the deaf and dumb admits a lot of obstacles in day to day life in communicating with their acquaintances. The most recent study done by the World Health Organization reports that very large section (around 360 million folks) present in the world have hearing loss, i.e. 5.3% of the earth's total population. This gives us a need for the invention of an automated system which converts hand gestures into meaningful words and sentences. The Convolutional Neural Network (CNN) is used on 24 hand signals of American Sign Language in order to enhance the ease of communication. OpenCV was used in order to follow up on further execution techniques like image preprocessing. The results demonstrated that CNN has an accuracy of 99.7% utilizing the database found on kaggle.com.

scholarly journals Deep Learning Based Indian Sign Language Words Identification System

2021 ◽  
Author(s):  
P. Golda Jeyasheeli ◽  
N. Indumathi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Sign Language ◽  
Language Identification ◽  
Identification System ◽  
Test Set ◽  
The People ◽  
Indian Sign Language ◽  
Smart Wearable

In Indian Population there is about 1 percent of the people are deaf and dumb. Deaf and dumb people use gestures to interact with each other. Ordinary humans fail to grasp the significance of gestures, which makes interaction between deaf and mute people hard. In attempt for ordinary citizens to understand the signs, an automated sign language identification system is proposed. A smart wearable hand device is designed by attaching different sensors to the gloves to perform the gestures. Each gesture has unique sensor values and those values are collected as an excel data. The characteristics of movements are extracted and categorized with the aid of a convolutional neural network (CNN). The data from the test set is identified by the CNN according to the classification. The objective of this system is to bridge the interaction gap between people who are deaf or hard of hearing and the rest of society.

scholarly journals Diagnosing Chronic Glaucoma Using Watershed and Convolutional Neural Network

2021 ◽  
Vol 9 (9) ◽  
pp. 272-283
Author(s):  
Naureen Fathima
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Three Dimensional ◽  
Image Plane ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computational Technique ◽  
Fundus Imaging

Abstract: Glaucoma is a disease that relates to the vision of human eye,Glaucoma is a disease that affects the human eye's vision. This sickness is regarded as an irreversible condition that causes eyesight degeneration. One of the most common causes of lifelong blindness is glaucoma in persons over the age of 40. Because of its trade-off between portability, size, and cost, fundus imaging is the most often utilised screening tool for glaucoma detection. Fundus imaging is a two-dimensional (2D) depiction of the three-dimensional (3D), semitransparent retinal tissues projected on to the imaging plane using reflected light. The idea plane that depicts the physical display screen through which a user perceives a virtual 3D scene is referred to as the "image plane”. The bulk of current algorithms for autonomous glaucoma assessment using fundus images rely on handcrafted segmentation-based features, which are influenced by the segmentation method used and the retrieved features. Convolutional neural networks (CNNs) are known for, among other things, their ability to learn highly discriminative features from raw pixel intensities. This work describes a computational technique for detecting glaucoma automatically. The major goal is to use a "image processing technique" to diagnose glaucoma using a fundus image as input. It trains datasets using a convolutional neural network (CNN). The Watershed algorithm is used for segmentation and is the most widely used technique in image processing. The following image processing processes are performed: region of interest, morphological procedures, and segmentation. This technique can be used to determine whether or not a person has Glaucoma. Keywords: Recommender system, item-based collaborative filtering, Natural Language Processing, Deep learning.

scholarly journals Online Hand Gesture Recognition and Classification for Deaf and Dumb

2021 ◽  
Vol 9 (VI) ◽  
pp. 4402-4404
Author(s):  
U. Mamatha
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sign Language ◽  
Gesture Recognition ◽  
Reference Image ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Network Layer ◽  
Web Camera ◽  
The Web

As sign language is used by deaf and dumb but the non-sign-language speaker cannot understand there sign language to overcome the problem we proposed this system using python. In this first we taken the some of the hand gestures are captured using the web camera. The image is pre-processed and then feature are extracted from the captured image .comparing the feature extracted image with the reference image. If matched decision is taken the displayed as a text. This helps the non-sign-language members to recognize easily by using Convolutional neural network layer (CNN) with tensor flow

scholarly journals Pneumonia Classification of Thorax Images using Convolutional Neural Networks

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 62
Author(s):  
Mahmud Suyuti ◽  
Endang Setyati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Technology Development ◽  
Image Data ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computing Technology ◽  
Correction Technique ◽  
High Level

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN).  The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.

scholarly journals Hand Gesture-based Sign Alphabet Recognition and Sentence Interpretation using a Convolutional Neural Network

2020 ◽  
Vol 4 (4) ◽  
pp. 20-27
Author(s):  
Md. Abdur Rahim ◽  
Jungpil Shin ◽  
Keun Soo Yun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
American Sign Language ◽  
Sign Language ◽  
Low Cost ◽  
Input Image ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Segmentation Technique ◽  
Wide Range

Sign language (SL) recognition is intended to connect deaf people with the general population via a variety of perspectives, experiences, and skills that serve as a basis for the development of human-computer interaction. Hand gesture-based SL recognition encompasses a wide range of human capabilities and perspectives. The efficiency of hand gesture performance is still challenging due to the complexity of varying levels of illumination, diversity, multiple aspects, self-identifying parts, different shapes, sizes, and complex backgrounds. In this context, we present an American Sign Language alphabet recognition system that translates sign gestures into text and creates a meaningful sentence from continuously performed gestures. We propose a segmentation technique for hand gestures and present a convolutional neural network (CNN) based on the fusion of features. The input image is captured directly from a video via a low-cost device such as a webcam and is pre-processed by a filtering and segmentation technique, for example the Otsu method. Following this, a CNN is used to extract the features, which are then fused in a fully connected layer. To classify and recognize the sign gestures, a well-known classifier such as Softmax is used. A dataset is proposed for this work that contains only static images of hand gestures, which were collected in a laboratory environment. An analysis of the results shows that our proposed system achieves better recognition accuracy than other state-of-the-art systems.

scholarly journals Pneumonia Classification of Thorax Images using Convolutional Neural Networks

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 62-68
Author(s):  
Mahmud Suyuti ◽  
Endang Setyati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Technology Development ◽  
Image Data ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computing Technology ◽  
Correction Technique ◽  
High Level

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN).  The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.

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