Text recognition from distorted or degraded images especially handwritten is a challenge in computing . The present research work on “Enhancement Of Degraded Text Images & Performance Comparison", is an excellent effort to enhance the degraded text images caused by poor scanning, historical papers, and other causes. This study will propose a technique for the enhancement of such a degraded text document images to improve their display quality characteristics by using thresh hold values as well as a comparison of performance among them. Images believed to be representative of the same symbol which occurs in different positions over an image source are clustered together. Using the symbols within a particular cluster, an average character image outline for that cluster of symbols is derived and thereafter used to refine the matching of symbols within the cluster and to determine a final representative symbol for that cluster. Thus the partially visible or distorted text can be recognized. The work will definitely prove a helping hand to young researchers for developing new ideas and algorithms to resolve the practical problems of text image degradation and their recovery.
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Prof. Vijay Kumar Sinha received his Ph.D. from IKGPTU, Kapurthala in Computer Science & Engineering. He received M.Tech. degree from Lovely Professional University in CSE, M.Tech.(IT) from Punjabi University, Patiala & B.Tech. (CSE) from Thapar University, Patiala. He is serving as Professor at Chandigarh Engineering College, Landran, Mohali.
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Paperback. Zustand: Brand New. 68 pages. 8.66x5.91x0.16 inches. In Stock. Artikel-Nr. zk613999926X
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Taschenbuch. Zustand: Neu. Enhancement of Degraded Text Images & Performance Comparison | A Research work on quality Enhancement of Poor Degraded Images | Vijay Kumar Sinha (u. a.) | Taschenbuch | 68 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139999262 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Artikel-Nr. 115385702
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