Abstrait

RECOGNITION OF SIGNBOARD IMAGES OF GURMUKHI

Er.Puneet kaur and Er.Balwinder Singh

Recently there is growing trend among worldwide researchers to recognize handwritten characters of many languages and scripts. Much of research work is done in English, Chinese and Japanese like languages. However, on Indian scripts, the research work is comparatively lagging. The work on other Indian scripts is in beginning stage. In this thesis work I have proposed recognition of isolated handwritten numerals of Gurumukhi script. In numerals, handwritten samples of 10 digits from different writers are considered. I have taken all these samples on white papers written in an isolated manner. The dataset used to recognize numerals is collected from 15 different writers each contributing 10 samples of each digit. After scanning, in preprocessing stage, the samples are converted to gray scale images. Then gray scale image is converted into binary image. I also applied median filtration, dilation, removal of noise having less than 30 pixels, some morphological operations to bridge unconnected pixels, to remove isolated pixels, to smooth pixel boundary, and to remove spur pixels. We segmented these samples in isolated and clipped images of each character based on white spaced pixels used for separation. In this paper, I have proposed recognition of isolated handwritten numerals of Gurumukhi script. In numerals, handwritten samples of 10 digits from different writers are considered. I have taken all these samples on white papers written in an isolated manner. The dataset used to recognize numerals is collected from 15 different writers each contributing 10 samples of each digit.