Parallel Implementation of Devanagari Document Image Segmentation Approach on GPU

Fast and accurate algorithms are necessary for Optical Character Recognition (OCR) systems to perform operations on document images such as pre-processing, segmentation, extracting features, training-testing of classifiers and post processing. The main goal of this research work is to make segmentation accurate and faster for processing of large numbers of Devnagari document images using parallel implementation of algorithm on Graphics Processing Unit (GPU). Proposed method employs extensive usage of highly multithreaded architecture and shared memory of multi-cored GPU. An efficient use of shared memory is required to optimize parallel reduction in Compute Unified Device Architecture (CUDA). Proposed method achieved a speedup of 20x-30x over the serial implementation when running on a GPU named GeForce 9500 GT.