Multi-lingual Hybrid Handwritten Signature Recognition Based on Deep Residual Attention Network

Abstract

The writing styles of Uyghur, Kazak, Kirgiz and other ethnic minorities in Xinjiang are very similar, so it is extremely difficult to extract the effective features of handwritten signatures of different languages by hand. To solve this problem, a multi-lingual hybrid handwritten signature recognition method based on deep residuals attention network was proposed. Firstly, an offline handwritten signature database in Chinese, Uyghur, Kazak and Kirgiz was established, with a total of 8,000 signed images. Then, the signature image is pre-processed by grayscale, median filtering, binarization, blank edge removal, thinning and size normalization. Finally, transfer learning method is used to input the signature image into the deep residual network, and the high-dimensional features are extracted automatically by the fusion channel attention for classification. The experimental results show that the highest recognition accuracy of this method is 99.44% for multi-lingual hybrid handwritten signature database, which has a high application value.