  Owing to the rapid growth of touchscreen mobile terminals and pen-based
interfaces, handwriting-based writer identification systems are attracting
increasing attention for personal authentication, digital forensics, and other
applications. However, most studies on writer identification have not been
satisfying because of the insufficiency of data and difficulty of designing
good features under various conditions of handwritings. Hence, we introduce an
end-to-end system, namely DeepWriterID, employed a deep convolutional neural
network (CNN) to address these problems. A key feature of DeepWriterID is a new
method we are proposing, called DropSegment. It designs to achieve data
augmentation and improve the generalized applicability of CNN. For sufficient
feature representation, we further introduce path signature feature maps to
improve performance. Experiments were conducted on the NLPR handwriting
database. Even though we only use pen-position information in the pen-down
state of the given handwriting samples, we achieved new state-of-the-art
identification rates of 95.72% for Chinese text and 98.51% for English text.
