DeepWriterID: An End-to-End Online Text-Independent Writer Identification System
The rapid adoption of touchscreen mobile terminals and pen-based interfaces has increased the demand for handwriting-based writer identification systems, particularly in the areas personal authentication and digital forensics. However, most writer identification systems yield poor performance because of insufficient data and an inability to handle the various conditions inherent in handwriting samples. To address these problems, the authors introduce the end-to-end DeepWriterID system that employs a deep convolutional neural network (CNN) and incorporates a new method called DropSegment to achieve data augmentation and improve the generalized applicability of CNN. Experiments show DeepWriterID achieves accuracy rates of 95.72 percent for Chinese text and 98.51 percent for English text.