Indian Sign Language Recognition System for Differently-able People

Sign languages commonly develop in deaf communities, that can include interpreters and friends and families of deaf people as well as people who are deaf or hard of hearing themselves. Sign Language Recognition is one of the most growing fields of research today. There are Many new techniques that have been developed recently in these fields. Here in this paper, we will propose a system for conversion of Indian sign language to text using Open CV. OpenCV designed to generate motion template images that can be used to rapidly determine where that motion occurred, how that motion occurred, and in which direction it occurred. There is also support for static gesture recognition in OpenCV which can locate hand position and define orientation (right or left) in image and create hand mask image. In this we will use image processing in which captured image will be processed which are digital in nature by the digital computer. By this we will enhance the quality of a picture so that it looks better. Our aim is to design a human computer interface system that can recognize language of the deaf and dumb accurately

Human Upper Body Pose Region Estimation

The objective of this chapter is to estimate 2D human pose for action recognition and especially for sign language recognition systems which require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. We propose an approach that progressively reduces the search space for body parts and can greatly improve chance to estimate the HUB pose. This involves two contributions: (a) a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. (b) Scaling the extracted parts during body orientation was attained using partial estimation of face size. The outcome of the system makes it applicable for real-time applications such as sign languages recognition systems. The method is fully automatic and self-initializing using a Haar-like face region. The tracking the HUB pose is based on the face detection algorithm. Our evaluation was done mainly using 50 images from INRIA Person Dataset

Applying mean shift and motion detection approaches to hand tracking in sign language

Hand gesture recognition is very important to communicate in sign language. In this paper, an effective object tracking and hand gesture recognition method is proposed. This method is combination of two well-known approaches, the mean shift and the motion detection algorithm. The mean shift algorithm can track objects based on the color, then when hand passes the face occlusion happens. Several solutions such as the particle filter, kalman filter and dynamic programming tracking have been used, but they are complicated, time consuming and so expensive. The proposed method is so easy, fast, efficient and low cost. In the first step, the motion detection algorithm subtracts the previous frame from the current frame to obtain the changes between two images and white pixels (motion level) are detected by using the threshold level. Then the mean shift algorithm is applied for tracking the hand motion. Simulation results show this method is faster than two times to compared with the old common algorithm

New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component

License Plate recognition plays an important role on the traffic monitoring and parking management systems. In this paper, a fast and real time method has been proposed which has an appropriate application to find tilt and poor quality plates. In the proposed method, at the beginning, the image is converted into binary mode using adaptive threshold. Then, by using some edge detection and morphology operations, plate number location has been specified. Finally, if the plat has tilt, its tilt is removed away. This method has been tested on another paper data set that has different images of the background, considering distance, and angel of view so that the correct extraction rate of plate reached at 98.66%.Comment: 3rd IEEE International Conference on Computer and Knowledge Engineering (ICCKE 2013), October 31 & November 1, 2013, Ferdowsi Universit Mashha

Linguistically-driven framework for computationally efficient and scalable sign recognition

We introduce a new general framework for sign recognition from monocular video using limited quantities of annotated data. The novelty of the hybrid framework we describe here is that we exploit state-of-the art learning methods while also incorporating features based on what we know about the linguistic composition of lexical signs. In particular, we analyze hand shape, orientation, location, and motion trajectories, and then use CRFs to combine this linguistically significant information for purposes of sign recognition. Our robust modeling and recognition of these sub-components of sign production allow an efficient parameterization of the sign recognition problem as compared with purely data-driven methods. This parameterization enables a scalable and extendable time-series learning approach that advances the state of the art in sign recognition, as shown by the results reported here for recognition of isolated, citation-form, lexical signs from American Sign Language (ASL)