Classifying Handwritten Chinese Characters using Convolutional Neural Networks

Image recognition applications have been increasingly gaining popularity, as computer hardware was getting more powerful and cheaper. This increase in computational resources, led researchers even closer to their target on creating algorithms that could achieve high accuracy in image recognition tasks. These algorithms are applied in many different fields, such as in medical images analysis and object recognition in real-time applications.Previously studies have shown that among many image recognition algorithms, artificial neural networks and specifically deep neural networks, perform outstandingly due to their ability to recognize extremely accurate patterns, shapes and specific characteristics in an image.In this thesis project we are going to investigate a specialized type of Deep Neural Networks, called Convolutional Neural Networks or CNNs, which are designed specifically for image recognition tasks. Furthermore we will analyze their hyper parameters, as well as explore different architectures, in order to understand how these affect the accuracy and speed of the recognition. Finally we will present the results of the different tests, in terms of accuracy and validate them according to specific statistical metrics. For the purpose of our research, a data-set of handwritten Chinese characters was used