Recognition of sign languages has gained reasonable interest by the researchers in the last decade. An accurate sign language recognition system can facilitate more accurate communication of deaf and dumb people. The wide variety of Indian Sign Language (ISL) led to more challenging learning process. In the current work, three novel methods was reported to solve the problem of recognition of ISL gestures effectively by combining Neural Network (NN) with Genetic Algorithm (GA), Evolutionary algorithm (EA) and Particle Swarm Optimization (PSO) separately to attain novel NN-GA, NN-EA and NN-PSO methods; respectively. The input weight vector to the NN has been optimized gradually to achieve minimum error. The proposed methods performance was compared to NN and the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifiers. Several performance metrics such as the accuracy, precision, recall, F-measure and kappa statistic were calculated. The experimental results established that the proposed algorithm achieved considerable improvement over the performance of existing works in order to recognize ISL gestures. The NN-PSO outperformed the other approaches with 99.96 accuracy, 99.98 precision, 98.29 recall, 99.63 F-Measure and 0.9956 Kappa Statistic.

Indian Sign Language Recognition Using Optimized Neural Networks

Sign Language is a natural language which deaf community uses for communication. Sign Language (SL) is a subset of gestures or signs made with fingers, hands, arms, eyes, and head, face etc. Each gesture in SL has a meaning assigned to it. Understanding SL is nothing but understanding the meaning of these gestures. There exists a problem in communication when a person who completely relies on this gestural SL for communication tries to converse with a person who does not understand the SL. Every country has its own developed SL. In India, this language is called as "Indian Sign Language (ISL). This paper aims to develop an algorithm that will translate the ISL into English. This paper has implemented a system named as "Indian Sign Language (ISL) Translator using Gesture recognition algorithm". The system translates gestures made in ISL into English. The gestures that have been translated include numbers, alphabets and few phrases. The algorithm first performs data acquisition, then the pre-processing of gestures is performed to track hand movement using a combinational algorithm, and recognition is done using template matching. The database used for implementation has been self-created and includes total 130,000 videos; out of which 72,000 videos were used to create the system database and remaining 58,000 videos have been tested for checking the performance of the system. The accuracy of this system is as high as 97.5%.

Indian sign language translator using gesture recognition algorithm

The Sign Language is a method of communication for deaf-dumb people. This paper presents the Sign Language Recognition system capable of recognizing 26 gestures from the Indian Sign Language by using MATLAB. The proposed system having four modules such as: pre-processing and hand segmentation, feature extraction, sign recognition and sign to text and voice conversion. Segmentation is done by using image processing. Different features are extracted such as Eigen values and Eigen vectors which are used in recognition. The Principle Component Analysis (PCA) algorithm was used for gesture recognition and recognized gesture is converted into text and voice format. The proposed system helps to minimize communication barrier between deaf-dumb people and normal people.

Real time Sign Language Recognition using PCA

Sign Language detection by technology is an overlooked concept despite there being a large social group which could benefit by it. There are not many technologies which help in connecting this social group to the rest of the world. Understanding sign language is one of the primary enablers in helping users of sign language communicate with the rest of the society. Image classification and machine learning can be used to help computers recognize sign language, which could then be interpreted by other people. Convolutional neural networks have been employed in this paper to recognize sign language gestures. The image dataset used consists of static sign language gestures captured on an RGB camera. Preprocessing was performed on the images, which then served as the cleaned input. The paper presents results obtained by retraining and testing this sign language gestures dataset on a convolutional neural network model using Inception v3. The model consists of multiple convolution filter inputs that are processed on the same input. The validation accuracy obtained was above 90% This paper also reviews the various attempts that have been made at sign language detection using machine learning and depth data of images. It takes stock of the various challenges posed in tackling such a problem, and outlines future scope as well.

Sign Language Recognition Using Deep Learning on Custom Processed Static Gesture Images

Hand gestures are natural and intuitive communication way for the human being to interact with his environment. They serve to designate or manipulate objects, to enhance speech, or communicate in a noisy place. They can also be a separate language. Gestures can have different meanings according to the language or culture. They can also be a way to interact with machines. The subject of our research concerns the design and development of computer vision methods for recognizing hand gestures by a mobile device. We have proposed a system based on SVM for recognizing various hand gestures. The system consists of four steps: hand segmentation, smoothing, feature extraction and classification. The idea here is to allow the smartphone to perform all necessary steps to recognize gestures without the need to connect to a computer in which a database is located to perform training process. With this system, all steps can be done by the smartphone. In this paper, for image acquisition, frontal camera of the smartphone is used. After that frames are gotten from the video, the color sampling is done which is followed by making binary representation of the hand, and then contours representing the hand were described with convex polygons to get information about fingertips and finally the input gesture was recognized using proper classifier.

Real time hand gesture recognition system for android devices

Understanding human motions can be posed as a pattern recognition problem. Applications of pattern recognition in information processing problems are diverse ranging from Speech, Handwritten character recognition to medical research and astronomy. Humans express time-varying motion patterns (gestures), such as a wave, in order to convey a message to a recipient. If a computer can detect and distinguish these human motion patterns, the desired message can be reconstructed, and the computer can respond appropriately. This paper represents a framework for a human computer interface capable of recognizing gestures from the Indian sign language. The complexity of Indian sign language recognition system increases due to the involvement of both the hands and also the overlapping of the hands. Alphabets and numbers have been recognized successfully. This system can be extended for words and sentences Recognition is done with PCA (Principal Component analysis). This paper also proposes recognition with neural networks. Further it is proposed that number of finger tips and the distance of fingertips from the centroid of the hand can be used along with PCA for robustness and efficient results.

Divya Deora

Papers

Indian sign language recognition