# Attention based End to end network for Offline Writer Identification on Word level data

arXiv: https://arxiv.org/abs/2404.07602 · HTML: https://mlsanity.com/paper/2404.07602/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 70 references that are in the map.
- First posted on arXiv 11 Apr 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [Unsupervised Feature Learning for Writer Identification and Writer Retrieval](https://mlsanity.com/paper/1705.09369/) (2017)
- [Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health](https://mlsanity.com/paper/2202.12760/) (2022)
- [FragNet: Writer Identification using Deep Fragment Networks](https://mlsanity.com/paper/2003.07212/) (2020)
- [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)
- [GR-RNN: Global-Context Residual Recurrent Neural Networks for Writer Identification](https://mlsanity.com/paper/2104.05036/) (2021)
- [Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images](https://mlsanity.com/paper/1809.10954/) (2018)
- [Text-independent writer identification using convolutional neural network](https://mlsanity.com/paper/2009.04877/) (2020)
- [Semi-supervised Feature Learning For Improving Writer Identification](https://mlsanity.com/paper/1807.05490/) (2018)
- [Offline Writer Identification Using Convolutional Neural Network Activation Features](https://mlsanity.com/paper/2402.17029/) (2024)
- [Attention based Writer Independent Handwriting Verification](https://mlsanity.com/paper/2009.04532/) (2020)
- [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)

## Built on by (most cited first, 1 of 1)

- [Different Strokes for Different Folks: Writer Identification for Historical Arabic Manuscripts](https://mlsanity.com/paper/2604.22515/) (2026)

## Abstract (arXiv)

Writer identification due to its widespread application in various fields has gained popularity over the years. In scenarios where optimum handwriting samples are available, whether they be in the form of a single line, a sentence, or an entire page, writer identification algorithms have demonstrated noteworthy levels of accuracy. However, in scenarios where only a limited number of handwritten samples are available, particularly in the form of word images, there is a significant scope for improvement. In this paper, we propose a writer identification system based on an attention-driven Convolutional Neural Network (CNN). The system is trained utilizing image segments, known as fragments, extracted from word images, employing a pyramid-based strategy. This methodology enables the system to capture a comprehensive representation of the data, encompassing both fine-grained details and coarse features across various levels of abstraction. These extracted fragments serve as the training data for the convolutional network, enabling it to learn a more robust representation compared to traditional convolution-based networks trained on word images. Additionally, the paper explores the integration of an attention mechanism to enhance the representational power of the learned features. The efficacy of the proposed algorithm is evaluated on three benchmark databases, demonstrating its proficiency in writer identification tasks, particularly in scenarios with limited access to handwriting data.