DeepWriterID: An End-to-end Online Text-independent Writer Identification System

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Yang, Weixin, Jin, Lianwen, Liu, Manfei
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 22.12.2015
Subjects
Artificial neural networks
Feature maps
Forensic computing
Handwriting
Identification systems
Neural networks
On-line systems
Performance enhancement
Touch screens
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Summary: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.
ISSN:2331-8422