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

TL;DR

DeepWriterID is an end-to-end CNN-based handwriting identification system that introduces DropSegment for data augmentation and path signature features for improved accuracy, achieving state-of-the-art results on handwriting datasets.

Contribution

It presents a novel deep learning framework with DropSegment and path signature features for online writer identification, addressing data scarcity and feature design challenges.

Findings

Achieved 95.72% accuracy on Chinese handwriting

Achieved 98.51% accuracy on English handwriting

Introduced DropSegment for effective data augmentation

Abstract

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.