# Writer Identification and Writer Retrieval Based on NetVLAD with Re-ranking

arXiv: https://arxiv.org/abs/2012.06186 · HTML: https://mlsanity.com/paper/2012.06186/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Evaluating Synthetic Pre-Training for Handwriting Processing Tasks](https://mlsanity.com/paper/2304.01842/) (2023)
- [Towards Writer Retrieval for Historical Datasets](https://mlsanity.com/paper/2305.05358/) (2023)
- [Self-Supervised Vision Transformers for Writer Retrieval](https://mlsanity.com/paper/2409.00751/) (2024)
- [KaiRacters: Character-level-based Writer Retrieval for Greek Papyri](https://mlsanity.com/paper/2407.07536/) (2024)
- [Online Writer Retrieval with Chinese Handwritten Phrases: A Synergistic Temporal-Frequency Representation Learning Approach](https://mlsanity.com/paper/2412.11668/) (2024)
- [Towards the Influence of Text Quantity on Writer Retrieval](https://mlsanity.com/paper/2506.07566/) (2025)
- [Transparency Techniques for Neural Networks trained on Writer Identification and Writer Verification](https://mlsanity.com/paper/2506.16331/) (2025)
- [Beyond the Pipeline: Analyzing Key Factors in End-to-End Deep Learning for Historical Writer Identification](https://mlsanity.com/paper/2510.18671/) (2025)

## Abstract (arXiv)

This paper addresses writer identification and writer retrieval which is considered as a challenging problem in the document analysis and recognition field. In this work, a novel pipeline is proposed for the problem at hand by employing a unified neural network architecture consisting of the ResNet-20 as a feature extractor and an integrated NetVLAD layer, inspired by the vector of locally aggregated descriptors (VLAD), in the head of the latter part. Having defined this architecture, the triplet semi-hard loss function is used to directly learn an embedding for individual input image patches. Subsequently, generalized max-pooling technique is employed for the aggregation of embedded descriptors of each handwritten image. Also, a novel re-ranking strategy is introduced for the task of identification and retrieval based on k-reciprocal nearest neighbors, and it is shown that the pipeline can benefit tremendously from this step. Experimental evaluation has been done on the three publicly available datasets: the ICDAR 2013, CVL, and KHATT datasets. Results indicate that while we perform comparably to the state-of-the-art on the KHATT, our writer identification and writer retrieval pipeline achieves superior performance on the ICDAR 2013 and CVL datasets in terms of mAP.