A Robust Off-line Writer Identification Method

Off-line writer identification plays an important role in forensics and historical document analysis. The current well-known off-line writer identification approaches are based on local feature extraction. They rely heavily on data augmentation and global encoding for writer retrieval, and need a great number of written contents for writer recognition. This paper proposes a new off-line writer identification method, called DLS-CNN, which combines document line segmentation in terms of statistic and deep convolutional neural network. More precisely, handwriting documents are segmented into patches using document line segmentation at first. Secondly, an improved residual neural network serves as the identification model. Finally, the mean value of all local features vectors are used as final global features for writer identification. Experimental results on ICADAR2 013 and CVL benchmark datasets show that, due to the extracted robust local features, DLS-CNN achieves higher identification rate with fewer written contents, and better retrieval result without data augmentation and global encoding. All the experiment codes are available at https://github.com/shiming-chen/DLS-CNN. Copyright © 2020 Acta Automatica Sinica. All rights reserved.