Paper Contents
Abstract
Segmentation of text from badly degraded document images is a very challenging task due to the high interintervisitation between the document background and the foreground text of die rent document images. Image processing and pattern recognition algorithms take more time for execution on a single core processor. Graphics Processing Unit (GPU) is more popular now-a-days due to their speed, programmability, low cost and more inbuilt execution cores in it. The main goal of this research work is to make binarization faster for recognition of a large number of degraded document images on GPU. In this system we provide new image segmentation algorithm that each pixel in the image has its own threshold proposed. We are doing parallel work on a window of mn size and extract object pixel of text stroke of that window. The document text is further segmented by a local threshold that is estimated based on the intensities of detected text stroke edge pixels within a local window.
Copyright
Copyright © 2023 Prof. Sneha A. Khaire. This is an open access article distributed under the Creative Commons Attribution License.
