English Handwriting Identification Method Using an Improved VGG-16 Model

Handwriting identification determines whether a measured and a sample handwriting set are identical. It has been widely used in judicial identification, forensic science, and contract confirmation in the financial sector. Traditional English handwriting identification methods usually compare the features of a set of handwritings with those of models resulting in a lack of efficiency and accuracy. With the rapid development of deep neural network technology in recent years, extracting relevant features, using self-learning considerably improve the accuracy of handwriting identification are possible. The traditional VGG-16 model performs well in image classification. However, because of its sequentially connected network structure, its training time is usually long. Therefore, adjusting the parameters and achieving the desired expectation of handwriting identification are difficult. This paper proposes a CC-VGG network model based on the traditional VGG-16 convolutional neural network(CNN)model. By replacing some convolution layers with composite convolution ones, the automatic identification of English handwritings was realized. The proposed model achieved good performance on public CVL and ICDAR2013 data sets, and its average correct rate reached 92.7% and 86.9% respectively, which was higher than that of existing algorithms. In addition, a handwritten English image data set, EI130, was created, which contained 130 categories and a total of 26000 pictures. Its high accuracy was also achieved by the proposed model. By comparison with other algorithms, the superiority of the proposed algorithm in training time was proved. Additionally, the experimental results on multiple data sets verified the effectiveness and advancement of the proposed algorithm. © 2020, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.