Image denoising to enhance character recognition using deep learning

Abstract

Image denoising is an important decisive preliminary stage in character recognition. A character image properties are enhanced to make it easier to recognize. In particular, to optical character recognition, an efficient image denoising technique instantaneously increases character recognition accuracy. In this paper, we proposed implementing a deep convolutional neural network (DCNN). A relationship between a noisy character image to its clean counter-part are mapped using DCNN. The overall process is divided into two stages: noise type classification and image denoising. Firstly, the noise type classification identifies the types of noise, and based on this noise type, a particular denoising model is selected, which increases the image denoising performance. The denoising network inputs a noisy image and a target of its clean corresponding image during the training. After the mapping function is trained, the generated model performs character image denoising. To increase the denoising efficiency, the noisy color image is decomposed into its RGB band. Then, on each band, a trained mapping function perform image denoising irrespective of the other band. Finally, each block is assembled to generate a clean image. In this paper, the MNIST and Char74K dataset of handwritten digits diluted with artificial noise divided into ten types are used for experimentation. The superiority of our strategy over counter-parts is the adequate minimization of image deterioration image denoising. Our experimental results show that the proposed techniques perform better image denoising as compared to the existing methods, both in terms of image noise type classification and image denoising. The overall character recognition accuracy increased by 66% after performing the proposed denoising technique.