Sign Language Recognition Using Image Processing

We have proposed a method for real time Hand Gesture Recognition and feature extraction using a web camera. In this approach, the image is captured through webcam attached to the system. First the input image is preprocessed and threshold is used to remove noise from image and smoothen the image. After this apply region filling to fill holes in the gesture or the object of interest. This helps in improving the classification and recognition step. Then select the biggest blob (biggest binary linked object) in the image and remove all small object, this is done to remove extra unwanted objects or noise from image. When the preprocessing is complete the image is passed on to feature extraction phase.The test image is classified in nearest neighbor's class in training set. The classification results are displayed to user and through the windows text to speech API gesture is translated into speech as well. The training data set of images that is used has 5 gestures, each with 50 variations of a single gesture with different lighting conditions. The purpose of this is to improve the accuracy of classification