Image Denoising to enhance Character Recognition using Deep Learning

In this paper, we proposed implementing a Deep Convolutional Neural Network. A relationship between a noisy character image to its clean counter-part are mapped using Deep Convolutional Neural Network.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. 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.. Our experimental results show that the proposed techniques perform better image denoising ascompared 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.