A comparative Study of Classification Algorithm for Printed Telugu Character Recognition

Optical character recognition (OCR) refers to a process whereby printed documents are transformed into ASCII files for the purpose of compact storage, editing, fast retrieval, and other file manipulations through the use of a computer. Optical Character Recognition plays a key role in the field of digital image processing, pattern recognition and image restoration techniques. The key motivation for the development of OCR Systems is the need to cope with the enormous flood of paper in the form of documents, bank cheques, commercial forms, government records, credit card imprints and mail sorting, generated by expanding technological society. The previous work is primarily designed for Telugu and used the Uniform Sampling Method as the basis for extraction of low-level, structural and stroke-type features and also used the nearest neighbor classifier for classification. The accuracy rate was 96%. The Objective of the current project is to improve the accuracy using different type of hybrid classifiers. The proposed work uses nearest neighborhood procedure and k meansalgorithms to get better performance.