DeepWriterID: An End-to-end Online Text-independent Writer Identification System
This addresses writer identification for personal authentication and digital forensics, offering a novel method with strong performance gains.
The paper tackled writer identification from handwriting by introducing DeepWriterID, an end-to-end system using a CNN with DropSegment for data augmentation and path signature features, achieving state-of-the-art identification rates of 95.72% for Chinese and 98.51% for English text on the NLPR database.
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.