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

arXiv: https://arxiv.org/abs/1508.04945 · HTML: https://mlsanity.com/paper/1508.04945/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2015.

## Built on by (most cited first, 14 of 14)

- [Framing RNN as a kernel method: A neural ODE approach](https://mlsanity.com/paper/2106.01202/) (2021)
- [Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition](https://mlsanity.com/paper/2110.13008/) (2021)
- [Online handwriting, signature and touch dynamics: tasks and potential applications in the field of security and health](https://mlsanity.com/paper/2202.12693/) (2022)
- [Privacy-Preserving Biometric Verification with Handwritten Random Digit String](https://mlsanity.com/paper/2503.12786/) (2025)
- [Letter-level Online Writer Identification](https://mlsanity.com/paper/2112.02824/) (2021)
- [Siamese based Neural Network for Offline Writer Identification on word level data](https://mlsanity.com/paper/2211.14443/) (2022)
- [Writer adaptation for offline text recognition: An exploration of neural network-based methods](https://mlsanity.com/paper/2307.15071/) (2023)
- [Self-Supervised Representation Learning for Online Handwriting Text Classification](https://mlsanity.com/paper/2310.06645/) (2023)
- [Offline Text-Independent Writer Identification based on word level data](https://mlsanity.com/paper/2202.10207/) (2022)
- [Signature Isolation Forest](https://mlsanity.com/paper/2403.04405/) (2024)
- [Attention based End to end network for Offline Writer Identification on Word level data](https://mlsanity.com/paper/2404.07602/) (2024)
- [Online Writer Retrieval with Chinese Handwritten Phrases: A Synergistic Temporal-Frequency Representation Learning Approach](https://mlsanity.com/paper/2412.11668/) (2024)
- [Cross-codex Learning for Reliable Scribe Identification in Medieval Manuscripts](https://mlsanity.com/paper/2312.04296/) (2023)
- [Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification](https://mlsanity.com/paper/2501.11895/) (2025)

## Abstract (arXiv)

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.