Implementation of OCR using Convolutional Neural Network (CNN): A Survey

Recently, character recognition and deep learning have caught the attention of many researchers. Optical Character Recognition (OCR) usually takes an image of the character as input and generates the identical character as output. The important role that OCR does is to transform printed materials into digital text files. Convolutional Neural Network (CNN) is an influential model that is generous with bright results in optical character recognition (OCR). The state-of-the-art performance which exists in deep neural networks is usually used to handle frequently recognition and classification problems. Many applications are using it, for instance, robotics, traffic monitoring, articles digitization, etc. CNN is designed to adaptively and automatically learn features by using many kinds of layers (convolution layers, pooling layers, and fully connected layers). In this paper we will go through the advantages and recent usage of CNN in OCR and why it’s important to use it in handwritten and printed text recognition and what subjects we can use this technique for. Researchers are progressively using CNN for the machine-printed characters and recognition of handwritten, that is because CNN architectures are suitable for recognition tasks by inputting some images.