Filtering Approaches and Mish Activation Function Applied on Handwritten Chinese Character Recognition

Handwritten Chinese Characters (HCC) have recently received much attention as a global means of exchanging information and knowledge. The start of the information age has increased the number of paper documents that must be electronically saved and shared. The recognition accuracy of online handwritten Chinese characters has reached its limit as online characters are more straightforward than offline characters. Furthermore, online character recognition enables stronger involvement and flexibility than offline characters. Deep learning techniques, such as convolutional neural networks (CNN), have superseded conventional Handwritten Chinese Character Recognition (HCCR) solutions, as proven in image identification. Nonetheless, because of the large number of comparable characters and styles, there is still an opportunity to improve the present recognition accuracy by adopting different activation functions, including Mish, Sigmoid, Tanh, and ReLU. The main goal of this study is to apply a filter and activation function that has a better impact on the recognition system to improve the performance of the recognition CNN model. In this study, we implemented different filter techniques and activation functions in CNN to offline Chinese characters to understand the effects of the model's recognition outcome. Two CNN layers are proposed given that they achieve comparative performances using fewer-layer CNN. The results demonstrate that the Weiner filter has better recognition performance than the median and average filters. Furthermore, the Mish activation function performs better than the Sigmoid, Tanh, and ReLU functions.