Efficient Image Denoising for Effective Digitization using Image Processing Techniques and Neural Networks

K.G. Srinivasa, B.J. Sowmya, D. Pradeep Kumar and Chetan Shetty
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K.G. Srinivasa: CBP Government Engineering College, Jaffarpur, New Delhi, India
B.J. Sowmya: Computer Science and Engineering Department, M.S. Ramaiah Institute of Technology, Bangalore, India
D. Pradeep Kumar: M.S. Ramaiah Institute of Technology, Bangalore, India
Chetan Shetty: S. Ramaiah Institute of Technology, Bangalore, India

International Journal of Applied Evolutionary Computation (IJAEC), 2016, vol. 7, issue 4, 77-93

Abstract: Vast reserves of information are found in ancient texts, scripts, stone tablets etc. However due to difficulty in creating new physical copies of such texts, knowledge to be obtained from them is limited to those few who have access to such resources. With the advent of Optical Character Recognition (OCR) efforts have been made to digitize such information. This increases their availability by making it easier to share, search and edit. Many documents are held back due to being damaged. This gives rise to an interesting problem of removing the noise from such documents so it becomes easier to apply OCR on them. Here the authors aim to develop a model that helps denoise images of such documents retaining on the text. The primary goal of their project is to help ease document digitization. They intend to study the effects of combining image processing techniques and neural networks. Image processing techniques like thresholding, filtering, edge detection, morphological operations, etc. will be applied to pre-process images to yield higher accuracy of neural network models.

Date: 2016
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