Sign Language Recognition based on Hands symbols ClassificationTriloki Gupta
Communication is always having a great impact in every domain and how it is considered the meaning of the thoughts and expressions that attract the researchers to bridge this gap for every living being.
The objective of this project is to identify the symbolic expression through images so that the communication gap between a normal and hearing impaired person can be easily bridged.
Github Link:https://github.com/TrilokiDA/Hand_Sign_Language
This document describes the development of an automatic language translation software to aid communication between Indian Sign Language and spoken English using LabVIEW. The software aims to translate one-handed finger spelling input in Indian Sign Language alphabets A-Z and numbers 1-9 into spoken English audio output, and 165 spoken English words input into Indian Sign Language picture display output. It utilizes the camera and microphone of the device for image and speech acquisition, and performs vision and speech analysis for translation. The software is intended to help communication between deaf or speech-impaired individuals and those who do not understand sign language.
Hand gesture recognition system(FYP REPORT)Afnan Rehman
This document is a final year project report submitted by three students - Afnan Ur Rehman, Haseeb Anser Iqbal, and Anwaar Ul Haq - for their bachelor's degree in computer science. The report describes the development of a hand gesture recognition system using computer vision and machine learning techniques. Key aspects of the project include image acquisition using a webcam, preprocessing the images using techniques like filtering and noise removal, detecting and cropping the hand region, extracting HU moments features, training a classifier on sample gesture images, and classifying new images using KNN. The system is also able to translate recognized gestures to speech using text-to-speech.
The goal of this project is to provide a platform that allows for communication between able-bodied and disabled people or between computers and human beings. There has been great emphasis on Human-Computer-Interaction research to create easy-to-use interfaces by directly employing natural communication and manipulation skills of humans . As an important part of the body, recognizing hand gesture is very important for Human-Computer-Interaction. In recent years, there has been a tremendous amount of research on hand gesture recognition
Deaf and Dump Gesture Recognition SystemPraveena T
This presentation mainly tells about the problems of those people followed by solution and an overall view of various topics such as market overview,target customers,flow chart,technology used,cost analysis and finally future plans.
The project is about building a human-computer interaction system
using hand gesture by cheap alternative to depth camera. We present
a robust , efficient and real-time technique for depth mapping using
normal 2D -camera and Infrared LED arrays . We use HOG feature
based SVM classifiers to predict hand pose and dynamic hand gestures . The system also tracks hand movements and events like grabbing and
clicking bythe hand.
Human Computer Interaction, Gesture provides a way for computers to understand human body language, Deals with the goal of interpreting hand gestures via mathematical algorithms, Enables humans to interface with the machine (HMI) and interact naturally without any mechanical devices
Sign Language Recognition based on Hands symbols ClassificationTriloki Gupta
Communication is always having a great impact in every domain and how it is considered the meaning of the thoughts and expressions that attract the researchers to bridge this gap for every living being.
The objective of this project is to identify the symbolic expression through images so that the communication gap between a normal and hearing impaired person can be easily bridged.
Github Link:https://github.com/TrilokiDA/Hand_Sign_Language
This document describes the development of an automatic language translation software to aid communication between Indian Sign Language and spoken English using LabVIEW. The software aims to translate one-handed finger spelling input in Indian Sign Language alphabets A-Z and numbers 1-9 into spoken English audio output, and 165 spoken English words input into Indian Sign Language picture display output. It utilizes the camera and microphone of the device for image and speech acquisition, and performs vision and speech analysis for translation. The software is intended to help communication between deaf or speech-impaired individuals and those who do not understand sign language.
Hand gesture recognition system(FYP REPORT)Afnan Rehman
This document is a final year project report submitted by three students - Afnan Ur Rehman, Haseeb Anser Iqbal, and Anwaar Ul Haq - for their bachelor's degree in computer science. The report describes the development of a hand gesture recognition system using computer vision and machine learning techniques. Key aspects of the project include image acquisition using a webcam, preprocessing the images using techniques like filtering and noise removal, detecting and cropping the hand region, extracting HU moments features, training a classifier on sample gesture images, and classifying new images using KNN. The system is also able to translate recognized gestures to speech using text-to-speech.
The goal of this project is to provide a platform that allows for communication between able-bodied and disabled people or between computers and human beings. There has been great emphasis on Human-Computer-Interaction research to create easy-to-use interfaces by directly employing natural communication and manipulation skills of humans . As an important part of the body, recognizing hand gesture is very important for Human-Computer-Interaction. In recent years, there has been a tremendous amount of research on hand gesture recognition
Deaf and Dump Gesture Recognition SystemPraveena T
This presentation mainly tells about the problems of those people followed by solution and an overall view of various topics such as market overview,target customers,flow chart,technology used,cost analysis and finally future plans.
The project is about building a human-computer interaction system
using hand gesture by cheap alternative to depth camera. We present
a robust , efficient and real-time technique for depth mapping using
normal 2D -camera and Infrared LED arrays . We use HOG feature
based SVM classifiers to predict hand pose and dynamic hand gestures . The system also tracks hand movements and events like grabbing and
clicking bythe hand.
Human Computer Interaction, Gesture provides a way for computers to understand human body language, Deals with the goal of interpreting hand gestures via mathematical algorithms, Enables humans to interface with the machine (HMI) and interact naturally without any mechanical devices
Gesture Recognition Technology-Seminar PPTSuraj Rai
This document provides an overview of gesture recognition technology. It begins with introducing gestures as a form of non-verbal communication and defines gesture recognition as interpreting human gestures through mathematical algorithms. It then discusses the motivation for gesture recognition, including its naturalness and applications in overcoming interaction problems with traditional input devices. The document outlines different types of gestures, input devices like gloves and cameras, challenges like developing standardized gesture languages, and uses like sign language recognition, virtual controllers, and assisting disabled individuals. It concludes with references for further reading.
This document discusses the development of a sign language recognition system using computer vision and machine learning techniques. It begins with background on the need for such a system to help deaf individuals communicate using technology. The system works by detecting hand signs with a camera and identifying them using a convolutional neural network model. It follows a waterfall development approach with requirements including a laptop, Python software, and sufficient lighting. Benefits are helping learn sign language, while limitations include needing good lighting conditions. Future work could add subtitles to make the system more useful for media applications.
Gestures are an important form of non-verbal communication between humans and can also be used to create interfaces between humans and machines. There are several types of gestures including emblems, sign languages, gesticulation and pantomimes. Gesture recognition allows humans to interact with computers through motions of the body, especially hand movements. Some methods of gesture recognition include device-based techniques using sensors on gloves, vision-based techniques using cameras, and controller-based techniques using motion controllers. Gesture recognition has applications in areas such as virtual controllers, sign language translation, game interaction and robotic assistance.
The slide was prepared on the purpose of presentation of our project face detection highlighting the basics of theory used and project details like goal, approach. Hope it's helpful.
This project developed a gesture recognition application using machine learning algorithms. The application recognizes gestures without color markers by extracting features from images using Hu moments and training a Hidden Markov Model. Common gestures like "ok" and "peace" were mapped to tasks like switching slides. The system was tested and achieved 60% accuracy. Future work could involve adding more gestures and connecting it to other devices.
This document provides an introduction and overview of hand gesture recognition. It discusses what gestures are, how gesture recognition works to interpret human body language and enable natural human-computer interaction. It outlines the key modules involved, including image transformation techniques like frame extraction, blurring and color thresholding. Example hand gestures and applications are shown, along with the overall data flow and required hardware and software components.
The document discusses a project to develop a desktop application that converts sign language to speech and text to sign language. It aims to help communicate with deaf people by removing barriers. The team plans to use EmguCV and C# Speech Engine. It has created an application that converts signs to text using image processing. Future work includes completing the software to cover all words in Arabic sign language.
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
Presentation on Face detection and recognition - Credits goes to Mr Shriram, "https://www.hackster.io/sriram17ei/facial-recognition-opencv-python-9bc724"
Knowledge representation techniques face several issues including representing important attributes of objects, relationships between attributes, choosing the level of detail in representations, depicting sets of multiple objects, and determining appropriate structures as needed.
Hand Written Character Recognition Using Neural Networks Chiranjeevi Adi
This document discusses a project to develop a handwritten character recognition system using a neural network. It will take handwritten English characters as input and recognize the patterns using a trained neural network. The system aims to recognize individual characters as well as classify them into groups. It will first preprocess, segment, extract features from, and then classify the input characters using the neural network. The document reviews several existing approaches to handwritten character recognition and the use of gradient and edge-based feature extraction with neural networks. It defines the objectives and methods for the proposed system, which will involve preprocessing, segmentation, feature extraction, and classification/recognition steps. Finally, it outlines the hardware and software requirements to implement the system as a MATLAB application.
This document summarizes a seminar presentation on text-to-speech synthesis and voice stick devices. The presentation covers the introduction of speech synthesis and its challenges. It discusses the disadvantages of braille systems and introduces the voice stick device, how it works using optical character recognition to convert text to audio. The presentation discusses the working principles of text-to-speech systems and their architecture. It outlines the advantages of these systems and their applications in devices for the blind, smartphones, vehicles and more. The presentation concludes with a section on further research and development opportunities in this area.
This document discusses hand gesture recognition using an artificial neural network. It aims to classify hand gestures into five categories (pointing one to five fingers) using a supervised feed-forward neural network and backpropagation algorithm. The objective is to facilitate communication for deaf people by automatically translating hand gestures into text. The system requires software like Pandas, Numpy and Matplotlib as well as hardware with a quad core processor and 16GB RAM. It explains key concepts of neural networks like neurons, weights, biases, activation functions and their advantages in handling large datasets and inferring unseen relationships.
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
Our speech to text conversion project aims to help the nearly 20% of people worldwide with disabilities by allowing them to control their computer and share information using only their voice. The system uses acoustic and language models with a speech engine to recognize speech and convert it to text. It can perform operations like opening calculator and wordpad. Speech recognition has applications in areas like cars, healthcare, education and daily life. Accuracy depends on factors like vocabulary size, speaker dependence, and speech type (isolated, continuous). The system aims to improve accessibility while reducing costs.
This document provides an overview of deep learning including definitions, architectures, types of deep learning networks, and applications. It defines deep learning as a branch of machine learning that uses neural networks with multiple hidden layers to perform feature extraction and transformation without being explicitly programmed. The main architectures discussed are deep neural networks, deep belief networks, and recurrent neural networks. The types of deep learning networks covered include feedforward neural networks, recurrent neural networks, convolutional neural networks, restricted Boltzmann machines, and autoencoders. Finally, the document discusses several applications of deep learning across industries such as self-driving cars, natural language processing, virtual assistants, and healthcare.
Virtual Mouse using hand gesture recognitionMuktiKalsekar
This project is to develop a Virtual Mouse using Hand Gesture Recognition. Hand gestures are the most effortless and natural way of communication. The aim is to perform various operations of the cursor. Instead of using more expensive sensors, a simple web camera can identify the gesture and perform the action. It helps the user to interact with a computer without any physical or hardware device to control mouse operation.
This document describes a sign language to voice conversion glove project. The project aims to help facilitate communication between deaf/mute communities and others by translating sign language gestures into speech. The glove uses flex sensors along the fingers connected to a microcontroller that analyzes the gestures and triggers a voice processing chip to output the corresponding word or phrase. The system is powered by a voltage regulator and includes an LCD for feedback. It provides a low-cost and portable way to bridge the communication gap experience by those in the deaf/mute community.
1) The document discusses various methods used to teach English to deaf and dumb students, including oral/aural communication, manual communication, and total communication approaches.
2) It outlines techniques within each approach, such as lip reading and speech therapy for oral communication, and sign language, finger spelling, and cued speech for manual communication.
3) The document recommends total communication as the best method, which combines oral/aural, manual communication, and technologies like hearing aids and cochlear implants based on each student's needs.
Gesture Recognition Technology-Seminar PPTSuraj Rai
This document provides an overview of gesture recognition technology. It begins with introducing gestures as a form of non-verbal communication and defines gesture recognition as interpreting human gestures through mathematical algorithms. It then discusses the motivation for gesture recognition, including its naturalness and applications in overcoming interaction problems with traditional input devices. The document outlines different types of gestures, input devices like gloves and cameras, challenges like developing standardized gesture languages, and uses like sign language recognition, virtual controllers, and assisting disabled individuals. It concludes with references for further reading.
This document discusses the development of a sign language recognition system using computer vision and machine learning techniques. It begins with background on the need for such a system to help deaf individuals communicate using technology. The system works by detecting hand signs with a camera and identifying them using a convolutional neural network model. It follows a waterfall development approach with requirements including a laptop, Python software, and sufficient lighting. Benefits are helping learn sign language, while limitations include needing good lighting conditions. Future work could add subtitles to make the system more useful for media applications.
Gestures are an important form of non-verbal communication between humans and can also be used to create interfaces between humans and machines. There are several types of gestures including emblems, sign languages, gesticulation and pantomimes. Gesture recognition allows humans to interact with computers through motions of the body, especially hand movements. Some methods of gesture recognition include device-based techniques using sensors on gloves, vision-based techniques using cameras, and controller-based techniques using motion controllers. Gesture recognition has applications in areas such as virtual controllers, sign language translation, game interaction and robotic assistance.
The slide was prepared on the purpose of presentation of our project face detection highlighting the basics of theory used and project details like goal, approach. Hope it's helpful.
This project developed a gesture recognition application using machine learning algorithms. The application recognizes gestures without color markers by extracting features from images using Hu moments and training a Hidden Markov Model. Common gestures like "ok" and "peace" were mapped to tasks like switching slides. The system was tested and achieved 60% accuracy. Future work could involve adding more gestures and connecting it to other devices.
This document provides an introduction and overview of hand gesture recognition. It discusses what gestures are, how gesture recognition works to interpret human body language and enable natural human-computer interaction. It outlines the key modules involved, including image transformation techniques like frame extraction, blurring and color thresholding. Example hand gestures and applications are shown, along with the overall data flow and required hardware and software components.
The document discusses a project to develop a desktop application that converts sign language to speech and text to sign language. It aims to help communicate with deaf people by removing barriers. The team plans to use EmguCV and C# Speech Engine. It has created an application that converts signs to text using image processing. Future work includes completing the software to cover all words in Arabic sign language.
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
Presentation on Face detection and recognition - Credits goes to Mr Shriram, "https://www.hackster.io/sriram17ei/facial-recognition-opencv-python-9bc724"
Knowledge representation techniques face several issues including representing important attributes of objects, relationships between attributes, choosing the level of detail in representations, depicting sets of multiple objects, and determining appropriate structures as needed.
Hand Written Character Recognition Using Neural Networks Chiranjeevi Adi
This document discusses a project to develop a handwritten character recognition system using a neural network. It will take handwritten English characters as input and recognize the patterns using a trained neural network. The system aims to recognize individual characters as well as classify them into groups. It will first preprocess, segment, extract features from, and then classify the input characters using the neural network. The document reviews several existing approaches to handwritten character recognition and the use of gradient and edge-based feature extraction with neural networks. It defines the objectives and methods for the proposed system, which will involve preprocessing, segmentation, feature extraction, and classification/recognition steps. Finally, it outlines the hardware and software requirements to implement the system as a MATLAB application.
This document summarizes a seminar presentation on text-to-speech synthesis and voice stick devices. The presentation covers the introduction of speech synthesis and its challenges. It discusses the disadvantages of braille systems and introduces the voice stick device, how it works using optical character recognition to convert text to audio. The presentation discusses the working principles of text-to-speech systems and their architecture. It outlines the advantages of these systems and their applications in devices for the blind, smartphones, vehicles and more. The presentation concludes with a section on further research and development opportunities in this area.
This document discusses hand gesture recognition using an artificial neural network. It aims to classify hand gestures into five categories (pointing one to five fingers) using a supervised feed-forward neural network and backpropagation algorithm. The objective is to facilitate communication for deaf people by automatically translating hand gestures into text. The system requires software like Pandas, Numpy and Matplotlib as well as hardware with a quad core processor and 16GB RAM. It explains key concepts of neural networks like neurons, weights, biases, activation functions and their advantages in handling large datasets and inferring unseen relationships.
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
Our speech to text conversion project aims to help the nearly 20% of people worldwide with disabilities by allowing them to control their computer and share information using only their voice. The system uses acoustic and language models with a speech engine to recognize speech and convert it to text. It can perform operations like opening calculator and wordpad. Speech recognition has applications in areas like cars, healthcare, education and daily life. Accuracy depends on factors like vocabulary size, speaker dependence, and speech type (isolated, continuous). The system aims to improve accessibility while reducing costs.
This document provides an overview of deep learning including definitions, architectures, types of deep learning networks, and applications. It defines deep learning as a branch of machine learning that uses neural networks with multiple hidden layers to perform feature extraction and transformation without being explicitly programmed. The main architectures discussed are deep neural networks, deep belief networks, and recurrent neural networks. The types of deep learning networks covered include feedforward neural networks, recurrent neural networks, convolutional neural networks, restricted Boltzmann machines, and autoencoders. Finally, the document discusses several applications of deep learning across industries such as self-driving cars, natural language processing, virtual assistants, and healthcare.
Virtual Mouse using hand gesture recognitionMuktiKalsekar
This project is to develop a Virtual Mouse using Hand Gesture Recognition. Hand gestures are the most effortless and natural way of communication. The aim is to perform various operations of the cursor. Instead of using more expensive sensors, a simple web camera can identify the gesture and perform the action. It helps the user to interact with a computer without any physical or hardware device to control mouse operation.
This document describes a sign language to voice conversion glove project. The project aims to help facilitate communication between deaf/mute communities and others by translating sign language gestures into speech. The glove uses flex sensors along the fingers connected to a microcontroller that analyzes the gestures and triggers a voice processing chip to output the corresponding word or phrase. The system is powered by a voltage regulator and includes an LCD for feedback. It provides a low-cost and portable way to bridge the communication gap experience by those in the deaf/mute community.
1) The document discusses various methods used to teach English to deaf and dumb students, including oral/aural communication, manual communication, and total communication approaches.
2) It outlines techniques within each approach, such as lip reading and speech therapy for oral communication, and sign language, finger spelling, and cued speech for manual communication.
3) The document recommends total communication as the best method, which combines oral/aural, manual communication, and technologies like hearing aids and cochlear implants based on each student's needs.
This document covers an introduction to sign language including the types of sign language, who uses sign language, why sign language is important, and provides a reference. It discusses that sign language involves hand shapes, movements, and facial expressions to communicate instead of speaking. There are two main types - alternate sign language used in limited contexts and primary sign language which is the first language of deaf communities. Sign language is important for deaf people to exercise their rights and access information as it allows communication without spoken language.
This document discusses sign language recognition using hidden Markov models. It outlines preprocessing techniques like noise reduction and hand detection using skin detection and optical flow analysis. Feature extraction is then performed on the training data to create feature vectors for each sign. Hidden Markov models will be trained on these features to classify signs in new data, allowing computers to recognize basic American Sign Language letters and numbers through vision-based techniques. The current progress includes data collection, preprocessing, hand detection and feature extraction, while the remaining work is training the hidden Markov model for classification.
Sign language is a visual method of communication that uses manual gestures and body language to convey meaning. It naturally develops in deaf communities as their primary means of communicating with each other when oral communication is ineffective. While sign languages share common properties, there is no single universal sign language and different communities develop their own, with regional variations. Sign languages have their own grammar and syntax distinct from spoken languages.
The document describes a gesture vocalizer system that uses multiple microcontrollers and sensors to facilitate communication between deaf, dumb, and blind communities and others. The system can detect gestures using a data glove with bend sensors and tilt sensors, analyze the gestures to determine their meaning, synthesize speech corresponding to the gestures, and display the gesture on an LCD screen. It is designed to translate sign language and other gestures into voice and text to help different communities communicate with each other.
This document describes a sign language translation project using a glove. The goal of the project is to bridge communication between deaf/mute people and others by translating sign language gestures into text and speech using an inexpensive electronic device. The glove will contain flex sensors and an accelerometer to capture hand movements and gestures, which will then be recognized, translated, and output as text on an LCD display and audio from a speaker. A block diagram shows the overall architecture of the glove unit, detection unit, and other components like the power supply. The document discusses the motivation, prime idea, content layout, advantages, and limitations of the project.
Hand talk (assistive technology for dumb)- Sign language glove with voiceVivekanand Gaikwad
We propose a sign language glove which will assist those people who are suffering for any kind of speech defect to communicate through gesture. The glove will record all the gesture made by the user & then it will translate these gesture into audio form.
The Gesture Recognition Technology is rapidly growing technology and this PPT describes about the working of gesture recognition technology,the sub fields in it, its applications and the challenges it faces.
This document describes a sign recognition system to translate sign language gestures from deaf and dumb people into audible speech. The system uses Harris corner detection and SIFT to extract features from captured images of gestures. K-nearest neighbors algorithm is used to match features to a database of images and identify the sign. A serial board communication kit then converts the text result to speech using a text-to-speech module and speaker. Examples show the system correctly identifying the signs for letters A, B, and C from captured images.
Sign language has existed for a long time and hundreds of different sign languages have developed independently across the world. Sign languages have their own complex grammars and are not dependent on spoken languages. They are expressed through manual communication such as hand gestures and movements, as well as facial expressions. While difficult to write down, sign languages effectively convey ideas and are an important means of communication for deaf communities.
This document provides information about an Indian Sign Language learning software called Saarthak. It includes a visual dictionary with a huge collection of Indian Sign Language signs represented through images and videos. Each sign entry also includes discussions. The software aims to be an ideal resource for both learning and teaching Indian Sign Language. It is continually adding more signs and designing new services to empower the deaf community. The document encourages sharing feedback to help improve the software.
The document provides information about American Sign Language (ASL) courses offered at Lake Travis High School. It describes the ASL 1, 2, and 3 course descriptions and focuses on developing both expressive and receptive signing skills. Expectations for students include daily practice, using only ASL during class, and participating in extracurricular deaf events. Helpful study techniques involve finding a study partner, daily vocabulary review, and incorporating non-manual behaviors.
Sign language is a means of communication using bodily movements, especially of the hands and arms. There are 7 main types of sign language including for the deaf, deaf-blind, and communicating with animals. Sign languages vary by country with some of the major ones being American Sign Language, British Sign Language, Mexican Sign Language, and New Zealand Sign Language. Sign languages play an important role in society through interpretation, telecommunications, and some communities where prevalence of deafness is high. An example is given of William Moon's moon alphabet which combined various modes of communication including Braille and sign language into a poetic language.
The document provides an overview of definitions, causes, challenges, and educational approaches related to deafness and hearing loss. It defines deaf and hard of hearing according to IDEA and discusses the debate around oral vs. manual communication methods. The document also summarizes prevalence data, the importance of early identification, challenges associated with hearing loss, and strategies for teaching students with hearing impairments.
This mini lesson contains two parts. The first part is the history of American Sign Language. The second is learning the formation of the Manual Alphabet of American Sign Language.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document discusses the design and development of a robotic hand controlled by a glove. The robotic hand uses servos to mimic the motion of individual human fingers as controlled by sensors in the glove. It describes the components used - flex sensors in the glove, an Arduino microcontroller, and servos in the robotic hand. The document outlines the working principle and potential applications of this robotic hand system, such as in factories or for people with disabilities. It aims to develop a versatile robotic hand concept among business students.
Gesture control robot using accelerometer pptRajendra Prasad
This document describes a gesture control robot project that uses an accelerometer. The aim of the project is to control the movements and directions of vehicles like airplanes, trains and cars using MEMS technology. The transmitter module uses an accelerometer, comparator, encoder and RF transmitter. The receiver module uses an RF receiver, decoder, microcontroller and actuator motor driver. The accelerometer provides analog data about movement in the X, Y and Z directions. The comparator and encoder convert the analog data for transmission. The RF modules transmit and receive the signals. The microcontroller processes the received data and the actuator converts it to control vehicle movements based on hand gestures detected by the accelerometer.
This document summarizes a presentation on image processing. It introduces image processing and discusses acquiring images in digital formats. It covers various aspects of image processing like enhancement, restoration, and geometry transformations. Image processing techniques discussed include histograms, compression, analysis, and computer-aided detection. Color imaging and different image types are also introduced. The document concludes with mentioning some common image processing software.
Ai based character recognition and speech synthesisAnkita Jadhao
The document discusses an AI seminar on character recognition and speech synthesis. It describes how optical character recognition can convert scanned images or text into machine code, and speech synthesis can artificially produce human speech. It provides details on preprocessing techniques for character recognition, such as de-noising and binarization of images. It also explains the processes of text analysis, phoneme generation and prosody generation used in speech synthesis engines.
Optical character recognition for bangla handwritten textcullenlover19
This document discusses optical character recognition (OCR) for Bangla handwritten text. It notes that while OCR is widely used to convert text to electronic formats, OCR for handwritten Bangla is still rare due to the challenge of infinite variations in individual handwriting styles. The authors aim to create an OCR with considerable accuracy for handwritten Bangla text. They take a bottom-up approach, applying their methodology to specific samples and improving it based on performance until reaching a general solution. Their current focus is the segmentation step, and they have tested thinning/run-length reduction and projection methods, but faced issues with both.
This is what we do when we attend the workshop around 10 days in department of electronic and telecommunication engineering, King Mongkut's University of Technology Thonburi. This PBL is associated between KMUTT and SIT(Shibaura Institute of Technology)
The translation of human-to-human speech conversations brings a unique set of challenges with it. We will discuss the set of issues to deal with, and the set of techniques we developed to address them, and show the implementation as well as current limitations using the example of Skype Translator.
Multimodal deep learning is a contemporary technique of feature extraction from multiple modalities. Previously neural networks were able to analyze monolithic source of input such as audio or video or text. However, this new model multiplex multiple source of input and perform better quality prediction.
Big Data LDN 2018: AI MEETS MAIL PROCESSINGMatt Stubbs
Date: 13th November 2018
Location: AI Lab Theatre
Time: 11:50 - 12:20
Speaker: Alexandre Hubert
Organisation: Dataiku
About: While virtual assistants have never sounded more human and as cars become driverless, companies still have to deal with a massive amount of mail. From unsolicited mail and bills to registered mail, mail processing solutions are a necessity. In an effort to bring AI to mail processing, we will present a prototype we've developed for a client in the insurance industry. Using Computer Vision and Deep Learning techniques, it automatically processes typed and hand-written letters to send them to the correct department within the organization.
Using intel's real sense to create games with natural user interfaces justi...BeMyApp
As technology advances, more sophisticated ways of interfacing with it are emerging. Even though new tech strives to make our apps more intuitive and easy to use, designing interfaces for those apps is not quite as straight forward. We’ve learned a few rules and “gotchas” when working with gesture cameras that can help to make apps that use them easy and fun to use.
In this talk Justin described:
1. Different data types you can get from Intel® RealSense™ and how to get them
2. Designing an interface for a gesture camera
3. Using your hands, face, and voice as an interface
This document describes an automatic attendance system using face recognition. It discusses the following:
1. The system was created by a team of 4 students to automate attendance taking and save faculty time. It uses OpenCV and face detection algorithms.
2. The system works by training on images of students taken from the class and stored in the database. It then detects faces in new images or video and identifies the students to mark attendance.
3. Key technologies used include OpenCV for image processing, Tkinter for the GUI, Pandas and NumPy for data handling, and algorithms like Haar Cascade and LBPH for face detection and recognition.
Code quality directly impacts how easy or hard your job is. The higher the quality, the easier it is for anyone (including you) to quickly jump in and get to work. Where do you start? In this session, Tonya Mork will empower you to simplify your code while dramatically increasing its code quality.
It's all about building <human code>, code that is highly human readable and understandable.
This slide deck is from a session I gave for WPSessions. https://wpsessions.com/sessions/code-quality-makes-jobs-easier/
Character recognition of Devanagari characters using Artificial Neural Networkijceronline
This document summarizes a paper on character recognition of Devanagari script using artificial neural networks. It discusses the challenges in recognizing handwritten Devanagari characters due to variations in writing style and speed. It presents the methodology used which includes data acquisition, preprocessing, segmentation, feature extraction and classification. In feature extraction, techniques like Gabor transform are used to extract features. A probabilistic neural network is then used for classification. The methodology achieved a recognition rate of 80% for handwritten Devanagari characters.
Harnessing Artificial Intelligence in your Applications - Level 300Amazon Web Services
AWS offers a family of AI services that provide cloud-native Machine Learning and Deep Learning technologies allowing developers to build an entirely new generation of apps that can see, hear, speak, understand, and interact with the real world. In this session we take a look at Amazon Rekognition, Amazon Polly, and Amazon Lex.
Speakers:
Adam Larter, Developer Solutions Architect, Amazon Web Services
Alastair Cousins, Solutions Architect, Amazon Web Services
This document describes a project that aims to create a more immersive 3D gaming experience through the use of Kinect control and interactions beyond traditional peripherals. It allows the player to sketch levels, which are then generated in the game. Players can edit and create content using hand gestures and voice commands recognized through techniques like hidden Markov models. The goal is to involve more of the player's senses and allow for shared social experiences. Future work could enhance social aspects, test with additional hardware, and analyze immersion metrics and applications to other genres.
Immersive 3D Environment Using Kinect and Voice Commands Kinda Altarbouch
Implement an editor for 3D environment using Unity3D, Kinect and voice commands. The system enables the designer/gamer to design her own 3D world with own hands and own voice.
Presented at the STC Summit Conference in May, 2012, in Chicago. This presentation provides a broad overview of using graphics in technical communication, beginning with basic concepts, then a discussion of graphics types (raster, vector), formats (EPS, PNG, JPG), colors (RGB, CMYK, spot), and finally specific examples (screen captures, commercial press).
Describes my photography color management process for a presentation to the North Austin Pfotographic Society (NAPfS) club. Including camera color calibration, monitor calibration and printer profiles. by Miguel A. Ortiz
extracting and ranking product features in opinion documentswangheda
This is a presentation we made in the 2012 Spring Data Mining class of Tsinghua University. The presentation is about a paper by Lei Zhang, Bing Liu, Suk Hwan Lim, Eamonn O’Brien-Strain
AI Products - Accuracy vs. User ExperienceIstván Rechner
Presented on the regional tech excellence conference and an AI meetup. Showing multiple novel AI algorithms and how to measure and operate them when used in real production environment.
We spend so much time focusing on conventional programming. Everyone focuses on standards, code clarity, testing, and what gems to use. Let's chat about what's done before your fingers hit the keys. Let's talk about brainstorming, requirements, stakeholders, mock-ups, and writing solid user stories and acceptance tests with Cucumber. Every project has a story - how will your next one end?
There’s been a lot of recent work on representing words as vectors with neural networks. These representations referred to as “neural embeddings” or “word embeddings”.
dmapply: A functional primitive to express distributed machine learning algor...Bikash Chandra Karmokar
ddR is a package that introduces distributed data structures in R like darray, dframe, and dlist. It provides a standardized API for distributed iteration and data manipulation through functions like dmapply. ddR aims to make distributed computing in R easier to use with good performance by writing algorithms once that can run on different distributed backends like Spark, HPE Distributed R through its unified interface. Evaluation shows ddR algorithms have performance comparable or better than custom implementations and other machine learning libraries.
This document summarizes a software development project involving database and serial port connections, SMS sending and receiving, and voice and file transfer capabilities. The project was developed by Bikash Chandra Karmokar and Tanmoy Shaha and supervised by Dr. K.M. Azharul Hasan. It connects to a database, finds available serial ports, and allows sending and receiving SMS messages between a PC and mobile device. The project also aims to enable voice chat, file transfer, and image transfer between devices using Java programming languages and image editing software.
This document discusses various 3D display techniques. It describes techniques that require glasses like anaglyph, polarization and eclipse method. It also covers glasses-free techniques like guided light, lenticular screens and parallax barriers. The document notes the challenges with current auto-stereoscopic displays in terms of higher costs, reduced resolution and limited viewing angles.
Dev Dives: Mining your data with AI-powered Continuous DiscoveryUiPathCommunity
Want to learn how AI and Continuous Discovery can uncover impactful automation opportunities? Watch this webinar to find out more about UiPath Discovery products!
Watch this session and:
👉 See the power of UiPath Discovery products, including Process Mining, Task Mining, Communications Mining, and Automation Hub
👉 Watch the demo of how to leverage system data, desktop data, or unstructured communications data to gain deeper understanding of existing processes
👉 Learn how you can benefit from each of the discovery products as an Automation Developer
🗣 Speakers:
Jyoti Raghav, Principal Technical Enablement Engineer @UiPath
Anja le Clercq, Principal Technical Enablement Engineer @UiPath
⏩ Register for our upcoming Dev Dives July session: Boosting Tester Productivity with Coded Automation and Autopilot™
👉 Link: https://bit.ly/Dev_Dives_July
This session was streamed live on June 27, 2024.
Check out all our upcoming Dev Dives 2024 sessions at:
🚩 https://bit.ly/Dev_Dives_2024
MySQL InnoDB Storage Engine: Deep Dive - MydbopsMydbops
This presentation, titled "MySQL - InnoDB" and delivered by Mayank Prasad at the Mydbops Open Source Database Meetup 16 on June 8th, 2024, covers dynamic configuration of REDO logs and instant ADD/DROP columns in InnoDB.
This presentation dives deep into the world of InnoDB, exploring two ground-breaking features introduced in MySQL 8.0:
• Dynamic Configuration of REDO Logs: Enhance your database's performance and flexibility with on-the-fly adjustments to REDO log capacity. Unleash the power of the snake metaphor to visualize how InnoDB manages REDO log files.
• Instant ADD/DROP Columns: Say goodbye to costly table rebuilds! This presentation unveils how InnoDB now enables seamless addition and removal of columns without compromising data integrity or incurring downtime.
Key Learnings:
• Grasp the concept of REDO logs and their significance in InnoDB's transaction management.
• Discover the advantages of dynamic REDO log configuration and how to leverage it for optimal performance.
• Understand the inner workings of instant ADD/DROP columns and their impact on database operations.
• Gain valuable insights into the row versioning mechanism that empowers instant column modifications.
Enterprise Knowledge’s Joe Hilger, COO, and Sara Nash, Principal Consultant, presented “Building a Semantic Layer of your Data Platform” at Data Summit Workshop on May 7th, 2024 in Boston, Massachusetts.
This presentation delved into the importance of the semantic layer and detailed four real-world applications. Hilger and Nash explored how a robust semantic layer architecture optimizes user journeys across diverse organizational needs, including data consistency and usability, search and discovery, reporting and insights, and data modernization. Practical use cases explore a variety of industries such as biotechnology, financial services, and global retail.
How to Optimize Call Monitoring: Automate QA and Elevate Customer ExperienceAggregage
The traditional method of manual call monitoring is no longer cutting it in today's fast-paced call center environment. Join this webinar where industry experts Angie Kronlage and April Wiita from Working Solutions will explore the power of automation to revolutionize outdated call review processes!
MongoDB vs ScyllaDB: Tractian’s Experience with Real-Time MLScyllaDB
Tractian, an AI-driven industrial monitoring company, recently discovered that their real-time ML environment needed to handle a tenfold increase in data throughput. In this session, JP Voltani (Head of Engineering at Tractian), details why and how they moved to ScyllaDB to scale their data pipeline for this challenge. JP compares ScyllaDB, MongoDB, and PostgreSQL, evaluating their data models, query languages, sharding and replication, and benchmark results. Attendees will gain practical insights into the MongoDB to ScyllaDB migration process, including challenges, lessons learned, and the impact on product performance.
Automation Student Developers Session 3: Introduction to UI AutomationUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program: http://bit.ly/Africa_Automation_Student_Developers
After our third session, you will find it easy to use UiPath Studio to create stable and functional bots that interact with user interfaces.
📕 Detailed agenda:
About UI automation and UI Activities
The Recording Tool: basic, desktop, and web recording
About Selectors and Types of Selectors
The UI Explorer
Using Wildcard Characters
💻 Extra training through UiPath Academy:
User Interface (UI) Automation
Selectors in Studio Deep Dive
👉 Register here for our upcoming Session 4/June 24: Excel Automation and Data Manipulation: https://community.uipath.com/events/details
Test Management as Chapter 5 of ISTQB Foundation. Topics covered are Test Organization, Test Planning and Estimation, Test Monitoring and Control, Test Execution Schedule, Test Strategy, Risk Management, Defect Management
EverHost AI Review: Empowering Websites with Limitless Possibilities through ...SOFTTECHHUB
The success of an online business hinges on the performance and reliability of its website. As more and more entrepreneurs and small businesses venture into the virtual realm, the need for a robust and cost-effective hosting solution has become paramount. Enter EverHost AI, a revolutionary hosting platform that harnesses the power of "AMD EPYC™ CPUs" technology to provide a seamless and unparalleled web hosting experience.
For senior executives, successfully managing a major cyber attack relies on your ability to minimise operational downtime, revenue loss and reputational damage.
Indeed, the approach you take to recovery is the ultimate test for your Resilience, Business Continuity, Cyber Security and IT teams.
Our Cyber Recovery Wargame prepares your organisation to deliver an exceptional crisis response.
Event date: 19th June 2024, Tate Modern
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
Database Management Myths for DevelopersJohn Sterrett
Myths, Mistakes, and Lessons learned about Managing SQL Server databases. We also focus on automating and validating your critical database management tasks.
Guidelines for Effective Data VisualizationUmmeSalmaM1
This PPT discuss about importance and need of data visualization, and its scope. Also sharing strong tips related to data visualization that helps to communicate the visual information effectively.
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...TrustArc
Global data transfers can be tricky due to different regulations and individual protections in each country. Sharing data with vendors has become such a normal part of business operations that some may not even realize they’re conducting a cross-border data transfer!
The Global CBPR Forum launched the new Global Cross-Border Privacy Rules framework in May 2024 to ensure that privacy compliance and regulatory differences across participating jurisdictions do not block a business's ability to deliver its products and services worldwide.
To benefit consumers and businesses, Global CBPRs promote trust and accountability while moving toward a future where consumer privacy is honored and data can be transferred responsibly across borders.
This webinar will review:
- What is a data transfer and its related risks
- How to manage and mitigate your data transfer risks
- How do different data transfer mechanisms like the EU-US DPF and Global CBPR benefit your business globally
- Globally what are the cross-border data transfer regulations and guidelines
CTO Insights: Steering a High-Stakes Database MigrationScyllaDB
In migrating a massive, business-critical database, the Chief Technology Officer's (CTO) perspective is crucial. This endeavor requires meticulous planning, risk assessment, and a structured approach to ensure minimal disruption and maximum data integrity during the transition. The CTO's role involves overseeing technical strategies, evaluating the impact on operations, ensuring data security, and coordinating with relevant teams to execute a seamless migration while mitigating potential risks. The focus is on maintaining continuity, optimising performance, and safeguarding the business's essential data throughout the migration process
Day 4 - Excel Automation and Data ManipulationUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program: https://bit.ly/Africa_Automation_Student_Developers
In this fourth session, we shall learn how to automate Excel-related tasks and manipulate data using UiPath Studio.
📕 Detailed agenda:
About Excel Automation and Excel Activities
About Data Manipulation and Data Conversion
About Strings and String Manipulation
💻 Extra training through UiPath Academy:
Excel Automation with the Modern Experience in Studio
Data Manipulation with Strings in Studio
👉 Register here for our upcoming Session 5/ June 25: Making Your RPA Journey Continuous and Beneficial: https://community.uipath.com/events/details/uipath-lagos-presents-session-5-making-your-automation-journey-continuous-and-beneficial/
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdfleebarnesutopia
So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation.
In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.
2. Dedication
First of all we would like to remember the
deaf and dumb people of the world for
whom we tried to develop a Sign language
Recognizer (SLR).
3. Outline
• Sign language
• SLR & its necessity
• Helping process of SLR
• Working procedure of SLR
• Block Diagram of SLR
• BP training time & graph
• Recognition accuracy
• Limitations
• Future plan
• Papers
4. What is Sign Language ??
Communicating language
used primarily by deaf people.
Uses different medium such
as hands, face, or eyes rather
than vocal tract or ears for
communication purpose.
Communication using sign language
5. What is SLR ??
Sign language recognizer (SLR) is a tool for
recognizing sign language of deaf and dumb
people of the world.
6. Why we need SLR ??
Problems:
• About 2 million people are deaf in our world
• They are deprived from various social
activities
• They are under-estimated to our society
• Communication problem
8. How SLR help ?? An Example.....
Suppose a deaf customer
went to a shop. She is ??
trying to express her
demands to the
shopkeeper using sign
language but the
shopkeeper can not
understand her demands. shopkeeper Deaf customer
9. Continued..
SLR brings the solution for this problem>>
• SLR capture signs shown by deaf man
• Convert the signs to text
• This text is shown to shopkeeper
Now the shopkeeper can understand the deaf man’s demands
11. Continued..
Text to sign conversion
When shopkeeper replied to the deaf customer SLR
• Convert text to sign
• This sign is shown to the deaf customer
Now the deaf man can understand the shopkeeper’s speeches
18. How SLR works ??
Image processing &
sign detection
Normalization
Sign recognition
Sign to text conversion
19. Continued..
Image processing &
sign detection
• Image capture
• Skin color detection
Normalization
Sign recognition
Sign to text conversion
20. Continued..
Image processing &
sign detection
• Hand gesture detection
Normalization • Sign detection
Sign recognition
Sign to text conversion
21. Continued..
Image processing &
sign detection
• Reducing image
size
Normalization
Sign recognition
Sign to text conversion
200x200 30x33
22. Continued..
Image processing &
sign detection
• Backpropagation
implementation
Normalization
Sign recognition
Sign to text conversion
23. Continued..
Image processing &
sign detection • Converting sign
language to Bengali
or English text
Normalization
Sign recognition
v
Sign to text conversion
25. BP Training
Figure: Training error versus number of iteration
26. Training time for BP
Training
Input size of pixel Time
(min)
30*33 1.5
45*48 2.8
60*63 3.7
We have used 50 signs as training input where each
sign has 5 samples that make 50 x 5 = 250 samples.
28. Limitations
• Due to brightness and contrast
sometimes webcam can hardly detect
the expected skin color.
• Because of the similarity of tracking
environment background color and skin
color the SLR gets unexpected pixels.
31. Future Plan
• Real time word recognition of ASL & BSL
• Implementing neural network Ensembles
• Implementing Genetic algorithm for sign
recognition
32. Required Tools
• Visual studio 2008
• XML
• Avro Keyboard installed
• Aforge .Net
• Open CV
• Webcam