# DeepWriter: A Multi-Stream Deep CNN for Text-independent Writer Identification

arXiv: https://arxiv.org/abs/1606.06472 · HTML: https://mlsanity.com/paper/1606.06472/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2016.

## Built on by (most cited first, 13 of 13)

- [GR-RNN: Global-Context Residual Recurrent Neural Networks for Writer Identification](https://mlsanity.com/paper/2104.05036/) (2021)
- [Exploiting Multi-Scale Fusion, Spatial Attention and Patch Interaction Techniques for Text-Independent Writer Identification](https://mlsanity.com/paper/2111.10605/) (2021)
- [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)
- [Offline Text-Independent Writer Identification based on word level data](https://mlsanity.com/paper/2202.10207/) (2022)
- [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)
- [Global Regular Network for Writer Identification](https://mlsanity.com/paper/2201.05951/) (2022)
- [Cross-codex Learning for Reliable Scribe Identification in Medieval Manuscripts](https://mlsanity.com/paper/2312.04296/) (2023)
- [Expansion Quantization Network: An Efficient Micro-emotion Annotation and Detection Framework](https://mlsanity.com/paper/2411.06160/) (2024)
- [DohaScript: A Large-Scale Multi-Writer Dataset for Continuous Handwritten Hindi Text](https://mlsanity.com/paper/2602.18089/) (2026)
- [Different Strokes for Different Folks: Writer Identification for Historical Arabic Manuscripts](https://mlsanity.com/paper/2604.22515/) (2026)
- [InkShield: Writing Style Protection Against Unauthorized Handwriting Mimicry](https://mlsanity.com/paper/2607.26976/) (2026)

## Abstract (arXiv)

Text-independent writer identification is challenging due to the huge variation of written contents and the ambiguous written styles of different writers. This paper proposes DeepWriter, a deep multi-stream CNN to learn deep powerful representation for recognizing writers. DeepWriter takes local handwritten patches as input and is trained with softmax classification loss. The main contributions are: 1) we design and optimize multi-stream structure for writer identification task; 2) we introduce data augmentation learning to enhance the performance of DeepWriter; 3) we introduce a patch scanning strategy to handle text image with different lengths. In addition, we find that different languages such as English and Chinese may share common features for writer identification, and joint training can yield better performance. Experimental results on IAM and HWDB datasets show that our models achieve high identification accuracy: 99.01% on 301 writers and 97.03% on 657 writers with one English sentence input, 93.85% on 300 writers with one Chinese character input, which outperform previous methods with a large margin. Moreover, our models obtain accuracy of 98.01% on 301 writers with only 4 English alphabets as input.