Hand Sign Recognition for Medical Field Genesis

For many deaf and dumb people, sign language is the principle means of communication. Normal people in such cases end up facing problems while communicating with speech impaired people. In our proposed system, we can automatically recognize sign language to help normal people to communicate more effectively with speech impaired people. This system recognizes the hand signs with the help of specially designed gloves. These recognized gestures are translated into a text and voice in real time. Thus this system reduces the communication gap between normal and the speech impaired people.

The Proposed system introduces an efficient and fast algorithm for identification of the number of fingers opened in a gesture representing an alphabet of the Binary Sign Language. The idea consisted of designing and building up an intelligent system using group of Flex sensor, machine learning and artificial intelligence concepts to take visual inputs of sign language's hand gestures and generate easily recognizable form of outputs. The objective of this project is to develop an intelligent system which can act as a translator between the sign language and the spoken language dynamically and can make the communication between people with hearing impairment and normal people both effective and efficient. After recognizing the gesture the output are expressed in terms of voice and text for display.

18th International Conference on Pattern Recognition (ICPR'06), 2006

In this paper, an approach for deaf-people interfacing using computer vision is presented. The recognition of alphabetic static signs of the Spanish Sign Language is addressed. The proposed approach combines a number of norms to evaluate the distance of the current sign, to the sign models stored in a database (a dictionary). This solution leads to a largely selective criterion. The method is simple enough to provide real-time recognition, and works suitably for most letters.

AIP Conference Proceedings, 2022

In the course of the last hardly any year, there has been speedy extension in the amount of deaf and dumb due to birth flaws, setbacks and oral sicknesses. Since people who are absolutely can't talk with a common individual, they need to rely upon some kind of visual communication . Movement affirmation is the mathematical interpretation of a human development by a handling creation. Motion based correspondence gives the best correspondence stage to people who are almost hard of hearing or calm to talk with an ordinary person. Develop a steady hand movement affirmation system, which sees hand movements, by then changes over gestural pictures into voice. Also, perceive the continuous article or an item in the picture, this assists with learning or discover the item name. The thoughts included the planning and executing a framework utilizing machine learning, image processing and computer vision ideas to accept contribution as hand motions and create unmistakable outcomes in content and voice structure with 91% precision.

IRJET, 2022

Hand Gesture recognition system is one of the best technique has developed in last few year, the reason behind to get success is ability to cooperate with machine. Body gestures are consider most common way to communication along human and personal computer in virtual world. To developed effective algorithm to that can recognize, interpret, process, and simulate human affections. Advance computing has ability to humanize digital interactions. This pepper introduces application using Computer Vision(CV) to recognize hand gesture. A camera takes live footage and analysis gestures on the basis of analysis system takes decision and recognize gestures.

International Journal for Research in Applied Science and Engineering Technology, 2018

The only way the speech and hearing impaired (i.e dumb and deaf) people can communicate is by sign language. The main purpose of this project is to ease communication for mute people in situations where they have to interact with people who are unable to understand sign language. The main idea of this project is to build a system using which mute people can communicate with all other people using their normal gestures. The system does not require the background to be perfectly black. It works on any background. The project uses artificial intelligence, machine learning and image processing system to identify, sign language used by the deaf people to communicate and converts them into text so that normal people can understand.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

This project is mainly focuses the sign way of communication is one of the most effective communication tool for the people who are not able to speak or hear anything. It is also useful for the person who are able to speak but not able to hear or vice versa. Sign language is boon for the deaf and dumb people. Sign language is the combination of different gesture, shape and movement of hand, body and facial expression. With the help of sign language, these physical impaired people express their emotions and thoughts to other person. Hence sign language recognition has become empirical task. Since sign language consist of various movement and gesture of hand therefore the accuracy of sign language depends on the accurate recognition of hand gesture. This project present various method of hand gesture and sign language recognition proposed in the past by various researchers.

— Sign language recognition is one of the most growing fields of research today and it is the most natural way of communication for the people with hearing problems. A hand gesture recognition system can provide an opportunity for deaf persons to communicate with normal people without the need of an interpreter or intermediate. We are going to build a systems and methods for the automatic recognition of Marathi sign language. Through that we are providing teaching classes for the purpose of training the deaf sign user in Marathi. The system does require hand to be properly aligned to the camera and does not need any special colour markers, glove or wearable sensors. A large set of samples has been used in proposed system to recognize isolated words from the standard Marathi sign language which are taken in front of camera by different deaf sign user. In our proposed system, we intend to recognize some very basic elements of sign language and to translate them to text and vice versa. Keywords— Sign Language, Deaf Sign User, Sign and Text Database, Canny's Algorithm.

IRJET, 2022

Communicating with people is a fundamental form of interaction, and being able to do so with people who have hearing impairments has proven to be a significant challenge. As a result, we are using Sign language as a critical tool for bridging the communication gap between hearing and deaf people. Communicating through sign language is difficult, and people with hearing impairments struggle to share their feelings and opinions with people who do not speak that language. To address this issue, we require a versatile and robust product. We proposed a system that aims to translate hand signals into their corresponding words as accurately as possible using data collected and another research. This can be accomplished through the use of various machine learning algorithms and techniques for classifying data into words. For our proposed system, we are using the Convolutional neural networks (CNN) algorithm. The process entails being able to capture multiple images of various hand gestures using a web cam in this analysis, and the system being able to predict and display the corresponding letters/words of the captured images of hand gestures that have previously been trained with Audio. Once an image is captured, it goes through a series of procedures that include computer vision techniques such as gray-scale conversion, mask operation, and dilation. The main goal and purpose of this project will be to remove the barrier between deaf and dumb people and normal people.

IRJET, 2022

eople who are deaf or hard of hearing, as well as others who are unable to communicate verbally, use sign language to interact with people and within their communities. A group of modern languages known as sign languages use a visual-manual modality to convey information. Real-time finger-spelling recognition in Sign Language presents a conundrum that is examined. Using webcam photos, we produced a dataset for the usual hand gestures used in ISL as well as a dataset for the identification of 36 different gestures (alphabets and numbers). Using a hand motion as input, the system instantly displays the recognized character on the monitor screen. This human-computer interaction (HCI) project aims to recognize several alphabets (a-z), digits (0-9) and a number of common ISL hand gestures. We employed a Pre-Trained SSD Mobile net V2 architecture trained on our own dataset to apply Transfer learning to the issue. We developed a solid model that reliably categorises Sign language in the vast majority of cases. The use of sensors (such glove sensors) and other image processing methods (like the edge detection approach, the Hough Transform, and others) has been used in numerous research in the past in this field, but these technologies are rather expensive, and many people cannot afford them. Different human-computer interaction methods for posture recognition were researched and assessed during the project. An assortment of image processing strategies with human movement categorization were shown to be the best answer. The technology can identify selected Sign Language signs with an accuracy of 70–80% even in low light and without a controlled background. As a result, we developed this free and user-friendly app to help such people. Apart from a small number of people, not everyone is conversant in sign language, thus they could need an interpreter, which could be troublesome and expensive. By developing algorithms that can instantly foresee alphanumeric hand motions in sign language, this research aims to close the communication gap. The major objective of this project is to develop an intelligent computer-based system that will enable deaf people to efficiently communicate with others by using hand gestures.