The document discusses a method for recognizing Indian Sign Language (ISL) gestures using computer vision techniques. It proposes using Histogram of Oriented Gradients (HOG) to extract features from images of hand gestures, and then classifying the gestures using a Support Vector Machine (SVM) classifier. The method achieved an accuracy of 97.1% on a dataset of single-handed English alphabet signs. It aims to help communication between deaf and normal people by translating recognized signs to text and speech output.
VISION BASED HAND GESTURE RECOGNITION USING FOURIER DESCRIPTOR FOR INDIAN SIG...sipij
Indian Sign Language (ISL) interpretation is the major research work going on to aid Indian deaf and dumb people. Considering the limitation of glove/sensor based approach, vision based approach was considered for ISL interpretation system. Among different human modalities, hand is the primarily used modality to any sign language interpretation system so, hand gesture was used for recognition of manual
alphabets and numbers. ISL consists of manual alphabets, numbers as well as large set of vocabulary with grammar. In this paper, methodology for recognition of static ISL manual alphabets, number and static symbols is given. ISL alphabet consists of single handed and two handed sign. Fourier descriptor as a feature extraction method was chosen due the property of invariant to rotation, scale and translation. True
positive rate was achieved 94.15% using nearest neighbourhood classifier with Euclidean distance where
sample data were considered with different illumination changes, different skin color and varying distance
from camera to signer position.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Real-Time Letter Recognition Model for Arabic Sign Language Using Kinect an...INFOGAIN PUBLICATION
The objective of this research is to develop a supervised machine learning hand-gesturing model to recognize Arabic Sign Language (ArSL), using two sensors: Microsoft's Kinect with a Leap Motion Controller. The proposed model relies on the concept of supervised learning to predict a hand pose from two depth images and defines a classifier algorithm to dynamically transform gestural interactions based on 3D positions of a hand-joint direction into their corresponding letters whereby live gesturing can be then compared and letters displayed in real time. This research is motivated by the need to increase the opportunity for the Arabic hearing-impaired to communicate with ease using ArSL and is the first step towards building a full communication system for the Arabic hearing impaired that can improve the interpretation of detected letters using fewer calculations. To evaluate the model, participants were asked to gesture the 28 letters of the Arabic alphabet multiple times each to create an ArSL letter data set of gestures built by the depth images retrieved by these devices. Then, participants were later asked to gesture letters to validate the classifier algorithm developed. The results indicated that using both devices for the ArSL model were essential in detecting and recognizing 22 of the 28 Arabic alphabet correctly 100 %.
Hand gesture recognition system has received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. In this work Hand segmentation using color models is introduced for obtaining hand gestures or detecting user’s hand by color segmentation technique for faster, better, robust, accurate and real-time applications. There are many such color models available for human hand and human skin detection with relative advantages and disadvantages in the field of Image Processing. For the purpose of hand Segmentation mix model approach has been adopted for best results. For detection of Hand from an image. The proposed approach is found to be accurate and effective for multiple conditions
A SIGNATURE BASED DRAVIDIAN SIGN LANGUAGE RECOGNITION BY SPARSE REPRESENTATIONijnlc
Sign language is a visual-gestural language used by deaf-dumb people for communication. As normal people are unfamiliar of sign language, the hearing-impaired people find it difficult to communicate with them. The communication gap between the normal and the deaf-dumb people can be bridged by means of Human–Computer Interaction. The objective of this paper is to convert the Dravidian (Tamil) sign language into text. The proposed method recognizes 12 vowels, 18 consonants and a special character “Aytham” of Tamil language by a vision based approach. In this work, the static images of the hand signs are obtained a web/digital camera. The hand region is segmented by a threshold applied to the hue channel of the input image. Then the region of interest (i.e. from wrist to fingers) is segmented using the reversed horizontal projection profile and the Discrete Cosine transformed signature is extracted from the boundary of hand sign. These features are invariant to translation, scale and rotation. Sparse representation classifier is incorporated to recognize 31 hand signs. The proposed method has attained a maximum recognition accuracy of 71% in a uniform background.
VISION BASED HAND GESTURE RECOGNITION USING FOURIER DESCRIPTOR FOR INDIAN SIG...sipij
Indian Sign Language (ISL) interpretation is the major research work going on to aid Indian deaf and dumb people. Considering the limitation of glove/sensor based approach, vision based approach was considered for ISL interpretation system. Among different human modalities, hand is the primarily used modality to any sign language interpretation system so, hand gesture was used for recognition of manual
alphabets and numbers. ISL consists of manual alphabets, numbers as well as large set of vocabulary with grammar. In this paper, methodology for recognition of static ISL manual alphabets, number and static symbols is given. ISL alphabet consists of single handed and two handed sign. Fourier descriptor as a feature extraction method was chosen due the property of invariant to rotation, scale and translation. True
positive rate was achieved 94.15% using nearest neighbourhood classifier with Euclidean distance where
sample data were considered with different illumination changes, different skin color and varying distance
from camera to signer position.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Real-Time Letter Recognition Model for Arabic Sign Language Using Kinect an...INFOGAIN PUBLICATION
The objective of this research is to develop a supervised machine learning hand-gesturing model to recognize Arabic Sign Language (ArSL), using two sensors: Microsoft's Kinect with a Leap Motion Controller. The proposed model relies on the concept of supervised learning to predict a hand pose from two depth images and defines a classifier algorithm to dynamically transform gestural interactions based on 3D positions of a hand-joint direction into their corresponding letters whereby live gesturing can be then compared and letters displayed in real time. This research is motivated by the need to increase the opportunity for the Arabic hearing-impaired to communicate with ease using ArSL and is the first step towards building a full communication system for the Arabic hearing impaired that can improve the interpretation of detected letters using fewer calculations. To evaluate the model, participants were asked to gesture the 28 letters of the Arabic alphabet multiple times each to create an ArSL letter data set of gestures built by the depth images retrieved by these devices. Then, participants were later asked to gesture letters to validate the classifier algorithm developed. The results indicated that using both devices for the ArSL model were essential in detecting and recognizing 22 of the 28 Arabic alphabet correctly 100 %.
Hand gesture recognition system has received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. In this work Hand segmentation using color models is introduced for obtaining hand gestures or detecting user’s hand by color segmentation technique for faster, better, robust, accurate and real-time applications. There are many such color models available for human hand and human skin detection with relative advantages and disadvantages in the field of Image Processing. For the purpose of hand Segmentation mix model approach has been adopted for best results. For detection of Hand from an image. The proposed approach is found to be accurate and effective for multiple conditions
A SIGNATURE BASED DRAVIDIAN SIGN LANGUAGE RECOGNITION BY SPARSE REPRESENTATIONijnlc
Sign language is a visual-gestural language used by deaf-dumb people for communication. As normal people are unfamiliar of sign language, the hearing-impaired people find it difficult to communicate with them. The communication gap between the normal and the deaf-dumb people can be bridged by means of Human–Computer Interaction. The objective of this paper is to convert the Dravidian (Tamil) sign language into text. The proposed method recognizes 12 vowels, 18 consonants and a special character “Aytham” of Tamil language by a vision based approach. In this work, the static images of the hand signs are obtained a web/digital camera. The hand region is segmented by a threshold applied to the hue channel of the input image. Then the region of interest (i.e. from wrist to fingers) is segmented using the reversed horizontal projection profile and the Discrete Cosine transformed signature is extracted from the boundary of hand sign. These features are invariant to translation, scale and rotation. Sparse representation classifier is incorporated to recognize 31 hand signs. The proposed method has attained a maximum recognition accuracy of 71% in a uniform background.
Hand gesture recognition method arriving great consideration in latest few years since of its manifoldness application and facility to interrelate by machine efficiently during human computer interaction. This paper mainly focuses on the survey on Hand Gesture Recognition. The hand gestures give a divide complementary modality to speech for express ones data. Hand gesture is the method of non-verbal communiqué for human beings for its freer expressions much more other than the body parts. Hand gesture detection has greater significance in scheme a competent human computer interaction method. This paper emphasis on different hand gesture approaches, technologies and applications.
Translation of sign language using generic fourier descriptor and nearest nei...ijcisjournal
Sign languages are used all over the world as a primary means of communication by deaf people. Sign
language translation is a promising application for vision-based gesture recognition methods. Therefore, it
is need such a tool that can translate sign language directly. This paper aims to create a system that can
translate static sign language into textual form automatically based on computer vision. The method
contains three phases, i.e. segmentation, feature extraction, and recognition. We used Generic Fourier
Descriptor (GFD) as feature extraction method and K-Nearest Neighbour (KNN) as classification
approach to recognize the signs. The system was applied to recognize each 120 stored images in database
and 120 images which is captured real time by webcam. We also translated 5 words in video sequences.
The experiment revealed that the system can recognized the signs with about 86 % accuracy for stored
images in database and 69 % for testing data which is captured real time by webcam.
Movement Tracking in Real-time Hand Gesture RecognitionPranav Kulkarni
To translate the gesture performed by the user in a
video sequence into meaningful symbols/commands, feature
extraction is the first and most crucial step in such systems
which measures the detected hand positions and its movement
track. We propose an efficient approach based on inter-frame
difference (IDF) to handle the hand movement tracking, which
is shown to be more robust in the accuracy aspect compared to
skin-color based approaches. Computational efficiency is
another attractive property that our approach greatly
improves the processing frame rate to fulfil the demand of a
real-time hand gesture recognition system.
Hand and wrist localization approach: sign language recognition Sana Fakhfakh
This paper proposes a new hand detection and wrist localization method which presents an important step in the hand gesture recognizing process. The wrist localization step has not been given much attention and the existing works are limited and include many conditions. Our proposed approach was evaluated on a public dataset whose obtained results underscore its performance. We highlight through a comparative study with existing work, the superiority of our approach and the importance of the wrist localization step. We also propose to benefit from our proposed method which can be applied in the sign language recognition domain, and more precisely in the Arabic digit sign language recognition.
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative...ijtsrd
Sign Language SL is a medium of communication for physically disabled people. It is a gesture based language for communication of dumb and deaf people. These people communicate by using different actions of hands, where each different action means something. Sign language is the only way of conversation for deaf and dumb people. It is very difficult to understand this language for the common people. Hence sign language recognition has become an important task. There is a necessity for a translator to communicate with the world. Real time translator for sign language provides a medium to communicate with others. Previous methods employs sensor gloves, hat mounted cameras, armband etc. which has wearing difficulties and have noisy behaviour. To alleviate this problem, a real time gesture recognition system using Deep Learning DL is proposed. It enables to achieve improvements on the gesture recognition performance. Jeni Moni | Anju J Prakash ""A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training: Survey"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30032.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/30032/a-deep-neural-framework-for-continuous-sign-language-recognition-by-iterative-training-survey/jeni-moni
Automatic Isolated word sign language recognitionSana Fakhfakh
This paper suggests a new system to help the
deaf and the hearing-impaired community improve their
connection with the hearing world and communicate
freely. The most important thing in this system is
how to help the users be free and finally have a more
natural way of communication. For this reason, we
present a new process based on two levels: a static-level
aiming to extract the most head/hands key points and
a dynamic-level with the objective of accumulating the
key-point trajectory matrix. Also our proposed approach
takes into account the signer-independence constraint.
A SIGNUM database is applied in the classification
stage and our system performances have improved with
a 94.3% recognition rate. Furthermore, a reduction
in time processing is obtained when the removing of
redundant frame step is applied. The obtained results
prove the superiority of our system compared to the
state-of- the-art methods in terms of recognition rate and
execution time.
Character Recognition (Devanagari Script)IJERA Editor
Character Recognition is has found major interest in field of research and practical application to analyze and study characters in different languages using image as their input. In this paper the user writes the Devanagari character using mouse as a plotter and then the corresponding character is saved in the form of image. This image is processed using Optical Character Recognition in which location, segmentation, pre-processing of image is done. Later Neural Networks is used to identify all the characters by the further process of OCR i.e. by using feature extraction and post-processing of image. This entire process is done using MATLAB.
A mediator person is required for communication between deaf person and a second person. But a
mediator should know the sign language used by deaf person. But this is also not possible always since there are
multiple sign languages for multiple languages. It is difficult for a deaf person to understand what a second
person speaks. And therefore deaf person should keep track of lip movements of second person in order to know
what he is speaking. But the lip movements do not give proper efficiency and accuracy since the facial
expressions and speech might not match. To overcome the above problems we have proposed a system, an
Android Application for recognizing sign language using hand gesture with the facility for user to define and
upload their own sign language into the system. The features of this system are the real time conversion of
gesture to text and speech. For two-way communication between deaf person and second person, the speech of
second person is converted into text. The processing steps include: gesture extraction, gesture matching and
conversion of text to speech and vice-versa. The system is not only useful for deaf community but can also be
used by common people who migrate to different regions and do not know local language.
Optical character recognition (OCR) is process of classification of optical patterns contained in a digital image. The process of OCR Recognition involves several steps including pre-processing, segmentation, feature extraction, classification. Pre-processing is for done the basic operation on input image like noise reduction which remove the noisy signal from image. Segmentation stage for segment the given image into line by line and segment each character from segmented line. Future extraction calculates the characteristics of character. A Radial Basis Function Neural Network (RBFNN) is used to classification contains the database and does the comparison.
Development of Sign Signal Translation System Based on Altera’s FPGA DE2 BoardWaqas Tariq
The main aim of this paper is to build a system that is capable of detecting and recognizing the hand gesture in an image captured by using a camera. The system is built based on Altera’s FPGA DE2 board, which contains a Nios II soft core processor. Image processing techniques and a simple but effective algorithm are implemented to achieve this purpose. Image processing techniques are used to smooth the image in order to ease the subsequent processes in translating the hand sign signal. The algorithm is built for translating the numerical hand sign signal and the result are displayed on the seven segment display. Altera’s Quartus II, SOPC Builder and Nios II EDS software are used to construct the system. By using SOPC Builder, the related components on the DE2 board can be interconnected easily and orderly compared to traditional method that requires lengthy source code and time consuming. Quartus II is used to compile and download the design to the DE2 board. Then, under Nios II EDS, C programming language is used to code the hand sign translation algorithm. Being able to recognize the hand sign signal from images can helps human in controlling a robot and other applications which require only a simple set of instructions provided a CMOS sensor is included in the system.
PERFORMANCE EVALUATION OF STATISTICAL CLASSIFIERS USING INDIAN SIGN LANGUAGE ...IJCSEA Journal
Sign language is the key for communication between deaf people. The significance of sign language is accentuated by various research activities and the technical aspects will definitely improve the communication needs. General view based sign language recognition systems extract manual parameters by a single camera view because it seems to be user friendly and hardware complexity; however it needs a high accuracy classifier for classification and recognition purpose. The decision making of the system in this work employs Indian sign language datasets and the performance evaluation of the system is compared by deploying the K-NN, Naïve Bayes and PNN classifiers. Classification using an instance-based classifiercan be a simple matter of locating the instance space and labelling the unknown instance with the same class label as that of the located (known) neighbour. Classifier always tries to improve the classification rate by pushing classifiers into an optimised structure. In each hand posture, a measure of properties like area, mean intensity, centroid, perimeter and diameter are taken; the classifier then uses these properties to determine the sign in different angles. They estimate the probability that a sign belongs to each of the target classes that is fixed. The impact of such study may reflect the exploration for using such algorithms
in other similar applications such as text classification and the development of automated systems.
Hand gesture recognition method arriving great consideration in latest few years since of its manifoldness application and facility to interrelate by machine efficiently during human computer interaction. This paper mainly focuses on the survey on Hand Gesture Recognition. The hand gestures give a divide complementary modality to speech for express ones data. Hand gesture is the method of non-verbal communiqué for human beings for its freer expressions much more other than the body parts. Hand gesture detection has greater significance in scheme a competent human computer interaction method. This paper emphasis on different hand gesture approaches, technologies and applications.
Translation of sign language using generic fourier descriptor and nearest nei...ijcisjournal
Sign languages are used all over the world as a primary means of communication by deaf people. Sign
language translation is a promising application for vision-based gesture recognition methods. Therefore, it
is need such a tool that can translate sign language directly. This paper aims to create a system that can
translate static sign language into textual form automatically based on computer vision. The method
contains three phases, i.e. segmentation, feature extraction, and recognition. We used Generic Fourier
Descriptor (GFD) as feature extraction method and K-Nearest Neighbour (KNN) as classification
approach to recognize the signs. The system was applied to recognize each 120 stored images in database
and 120 images which is captured real time by webcam. We also translated 5 words in video sequences.
The experiment revealed that the system can recognized the signs with about 86 % accuracy for stored
images in database and 69 % for testing data which is captured real time by webcam.
Movement Tracking in Real-time Hand Gesture RecognitionPranav Kulkarni
To translate the gesture performed by the user in a
video sequence into meaningful symbols/commands, feature
extraction is the first and most crucial step in such systems
which measures the detected hand positions and its movement
track. We propose an efficient approach based on inter-frame
difference (IDF) to handle the hand movement tracking, which
is shown to be more robust in the accuracy aspect compared to
skin-color based approaches. Computational efficiency is
another attractive property that our approach greatly
improves the processing frame rate to fulfil the demand of a
real-time hand gesture recognition system.
Hand and wrist localization approach: sign language recognition Sana Fakhfakh
This paper proposes a new hand detection and wrist localization method which presents an important step in the hand gesture recognizing process. The wrist localization step has not been given much attention and the existing works are limited and include many conditions. Our proposed approach was evaluated on a public dataset whose obtained results underscore its performance. We highlight through a comparative study with existing work, the superiority of our approach and the importance of the wrist localization step. We also propose to benefit from our proposed method which can be applied in the sign language recognition domain, and more precisely in the Arabic digit sign language recognition.
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative...ijtsrd
Sign Language SL is a medium of communication for physically disabled people. It is a gesture based language for communication of dumb and deaf people. These people communicate by using different actions of hands, where each different action means something. Sign language is the only way of conversation for deaf and dumb people. It is very difficult to understand this language for the common people. Hence sign language recognition has become an important task. There is a necessity for a translator to communicate with the world. Real time translator for sign language provides a medium to communicate with others. Previous methods employs sensor gloves, hat mounted cameras, armband etc. which has wearing difficulties and have noisy behaviour. To alleviate this problem, a real time gesture recognition system using Deep Learning DL is proposed. It enables to achieve improvements on the gesture recognition performance. Jeni Moni | Anju J Prakash ""A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training: Survey"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30032.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/30032/a-deep-neural-framework-for-continuous-sign-language-recognition-by-iterative-training-survey/jeni-moni
Automatic Isolated word sign language recognitionSana Fakhfakh
This paper suggests a new system to help the
deaf and the hearing-impaired community improve their
connection with the hearing world and communicate
freely. The most important thing in this system is
how to help the users be free and finally have a more
natural way of communication. For this reason, we
present a new process based on two levels: a static-level
aiming to extract the most head/hands key points and
a dynamic-level with the objective of accumulating the
key-point trajectory matrix. Also our proposed approach
takes into account the signer-independence constraint.
A SIGNUM database is applied in the classification
stage and our system performances have improved with
a 94.3% recognition rate. Furthermore, a reduction
in time processing is obtained when the removing of
redundant frame step is applied. The obtained results
prove the superiority of our system compared to the
state-of- the-art methods in terms of recognition rate and
execution time.
Character Recognition (Devanagari Script)IJERA Editor
Character Recognition is has found major interest in field of research and practical application to analyze and study characters in different languages using image as their input. In this paper the user writes the Devanagari character using mouse as a plotter and then the corresponding character is saved in the form of image. This image is processed using Optical Character Recognition in which location, segmentation, pre-processing of image is done. Later Neural Networks is used to identify all the characters by the further process of OCR i.e. by using feature extraction and post-processing of image. This entire process is done using MATLAB.
A mediator person is required for communication between deaf person and a second person. But a
mediator should know the sign language used by deaf person. But this is also not possible always since there are
multiple sign languages for multiple languages. It is difficult for a deaf person to understand what a second
person speaks. And therefore deaf person should keep track of lip movements of second person in order to know
what he is speaking. But the lip movements do not give proper efficiency and accuracy since the facial
expressions and speech might not match. To overcome the above problems we have proposed a system, an
Android Application for recognizing sign language using hand gesture with the facility for user to define and
upload their own sign language into the system. The features of this system are the real time conversion of
gesture to text and speech. For two-way communication between deaf person and second person, the speech of
second person is converted into text. The processing steps include: gesture extraction, gesture matching and
conversion of text to speech and vice-versa. The system is not only useful for deaf community but can also be
used by common people who migrate to different regions and do not know local language.
Optical character recognition (OCR) is process of classification of optical patterns contained in a digital image. The process of OCR Recognition involves several steps including pre-processing, segmentation, feature extraction, classification. Pre-processing is for done the basic operation on input image like noise reduction which remove the noisy signal from image. Segmentation stage for segment the given image into line by line and segment each character from segmented line. Future extraction calculates the characteristics of character. A Radial Basis Function Neural Network (RBFNN) is used to classification contains the database and does the comparison.
Development of Sign Signal Translation System Based on Altera’s FPGA DE2 BoardWaqas Tariq
The main aim of this paper is to build a system that is capable of detecting and recognizing the hand gesture in an image captured by using a camera. The system is built based on Altera’s FPGA DE2 board, which contains a Nios II soft core processor. Image processing techniques and a simple but effective algorithm are implemented to achieve this purpose. Image processing techniques are used to smooth the image in order to ease the subsequent processes in translating the hand sign signal. The algorithm is built for translating the numerical hand sign signal and the result are displayed on the seven segment display. Altera’s Quartus II, SOPC Builder and Nios II EDS software are used to construct the system. By using SOPC Builder, the related components on the DE2 board can be interconnected easily and orderly compared to traditional method that requires lengthy source code and time consuming. Quartus II is used to compile and download the design to the DE2 board. Then, under Nios II EDS, C programming language is used to code the hand sign translation algorithm. Being able to recognize the hand sign signal from images can helps human in controlling a robot and other applications which require only a simple set of instructions provided a CMOS sensor is included in the system.
PERFORMANCE EVALUATION OF STATISTICAL CLASSIFIERS USING INDIAN SIGN LANGUAGE ...IJCSEA Journal
Sign language is the key for communication between deaf people. The significance of sign language is accentuated by various research activities and the technical aspects will definitely improve the communication needs. General view based sign language recognition systems extract manual parameters by a single camera view because it seems to be user friendly and hardware complexity; however it needs a high accuracy classifier for classification and recognition purpose. The decision making of the system in this work employs Indian sign language datasets and the performance evaluation of the system is compared by deploying the K-NN, Naïve Bayes and PNN classifiers. Classification using an instance-based classifiercan be a simple matter of locating the instance space and labelling the unknown instance with the same class label as that of the located (known) neighbour. Classifier always tries to improve the classification rate by pushing classifiers into an optimised structure. In each hand posture, a measure of properties like area, mean intensity, centroid, perimeter and diameter are taken; the classifier then uses these properties to determine the sign in different angles. They estimate the probability that a sign belongs to each of the target classes that is fixed. The impact of such study may reflect the exploration for using such algorithms
in other similar applications such as text classification and the development of automated systems.
Hand gesture recognition using support vector machinetheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.