# Letter-level Online Writer Identification

arXiv: https://arxiv.org/abs/2112.02824 · HTML: https://mlsanity.com/paper/2112.02824/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 61 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 20 of 20)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [NormFace: L2 Hypersphere Embedding for Face Verification](https://mlsanity.com/paper/1704.06369/) (2017)
- [An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data](https://mlsanity.com/paper/1611.06067/) (2016)
- [Dual Attention Networks for Multimodal Reasoning and Matching](https://mlsanity.com/paper/1611.00471/) (2016)
- [Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification](https://mlsanity.com/paper/1803.09937/) (2018)
- [Gated-Attention Readers for Text Comprehension](https://mlsanity.com/paper/1606.01549/) (2016)
- [Applying Deep Learning to Answer Selection: A Study and An Open Task](https://mlsanity.com/paper/1508.01585/) (2015)
- [DeepWriterID: An End-to-end Online Text-independent Writer Identification System](https://mlsanity.com/paper/1508.04945/) (2015)
- [DeepWriter: A Multi-Stream Deep CNN for Text-independent Writer Identification](https://mlsanity.com/paper/1606.06472/) (2016)
- [Encoding CNN Activations for Writer Recognition](https://mlsanity.com/paper/1712.07923/) (2017)
- [Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1702.06332/) (2017)
- [SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification](https://mlsanity.com/paper/1911.05358/) (2019)
- [Text-independent writer identification using convolutional neural network](https://mlsanity.com/paper/2009.04877/) (2020)
- [Offline Writer Identification based on the Path Signature Feature](https://mlsanity.com/paper/1905.01207/) (2019)
- [Online Signature Verification Based on Writer Specific Feature Selection and Fuzzy Similarity Measure](https://mlsanity.com/paper/1905.08574/) (2019)

## Built on by (most cited first, 3 of 3)

- [Privacy-Preserving Biometric Verification with Handwritten Random Digit String](https://mlsanity.com/paper/2503.12786/) (2025)
- [Attention based End to end network for Offline Writer Identification on Word level data](https://mlsanity.com/paper/2404.07602/) (2024)
- [Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification](https://mlsanity.com/paper/2501.11895/) (2025)

## Abstract (arXiv)

Writer identification (writer-id), an important field in biometrics, aims to identify a writer by their handwriting. Identification in existing writer-id studies requires a complete document or text, limiting the scalability and flexibility of writer-id in realistic applications. To make the application of writer-id more practical (e.g., on mobile devices), we focus on a novel problem, letter-level online writer-id, which requires only a few trajectories of written letters as identification cues. Unlike text- document-based writer-id which has rich context for identification, there are much fewer clues to recognize an author from only a few single letters. A main challenge is that a person often writes a letter in different styles from time to time. We refer to this problem as the variance of online writing styles (Var-O-Styles). We address the Var-O-Styles in a capture-normalize-aggregate fashion: Firstly, we extract different features of a letter trajectory by a carefully designed multi-branch encoder, in an attempt to capture different online writing styles. Then we convert all these style features to a reference style feature domain by a novel normalization layer. Finally, we aggregate the normalized features by a hierarchical attention pooling (HAP), which fuses all the input letters with multiple writing styles into a compact feature vector. In addition, we also contribute a large-scale LEtter-level online wRiter IDentification dataset (LERID) for evaluation. Extensive comparative experiments demonstrate the effectiveness of the proposed framework.