Sun, Y.; Fan, D.; Wu, H.; Wang, Z.; Tian, J. Offline Mongolian Handwriting Identification Based on Convolutional Neural Network. Electronics 2023, 13, 111, doi:10.3390/electronics13010111.
Sun, Y.; Fan, D.; Wu, H.; Wang, Z.; Tian, J. Offline Mongolian Handwriting Identification Based on Convolutional Neural Network. Electronics 2023, 13, 111, doi:10.3390/electronics13010111.
Sun, Y.; Fan, D.; Wu, H.; Wang, Z.; Tian, J. Offline Mongolian Handwriting Identification Based on Convolutional Neural Network. Electronics 2023, 13, 111, doi:10.3390/electronics13010111.
Sun, Y.; Fan, D.; Wu, H.; Wang, Z.; Tian, J. Offline Mongolian Handwriting Identification Based on Convolutional Neural Network. Electronics 2023, 13, 111, doi:10.3390/electronics13010111.
Abstract
Handwriting is a form of biometric behavioral characteristic with evident individual distinctiveness. With the surge of the deep learning trend and the demand for forensic identification, handwriting identification has become one of the focal points of research in the field of pattern recognition. The research on handwriting identification in major world languages has reached a mature stage. However, there is still a notable lack of relevant research in the field of Mongolian handwriting identification, despite the fact that Mongolian is used by over 4 million people in China. This paper embarks on an initial exploration of Mongolian handwriting identification by constructing a convolutional neural network named MWInet-12. In this paper, the model evaluation experiments were conducted using a dataset comprising 156,372 samples contributed by 125 authors from the MOLHW dataset. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The final results of the experiments reveal impressive accuracy on the test set, achieving a top-1 accuracy of 89.60% and a top-5 accuracy of 97.53%. Furthermore, through comparative experiments involving Resnet, Fragnet, and GRRNN models, this paper establishes that the proposed model yields the most favorable results for Mongolian handwriting identification.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
