# Matching Handwritten Document Images

arXiv: https://arxiv.org/abs/1605.05923 · HTML: https://mlsanity.com/paper/1605.05923/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2016.

## Built on by (most cited first, 7 of 7)

- [VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification](https://mlsanity.com/paper/2205.12029/) (2022)
- [ST-KeyS: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents](https://mlsanity.com/paper/2303.03127/) (2023)
- [Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting](https://mlsanity.com/paper/2106.05144/) (2021)
- [Handwritten text generation and strikethrough characters augmentation](https://mlsanity.com/paper/2112.07395/) (2021)
- [A Study of Augmentation Methods for Handwritten Stenography Recognition](https://mlsanity.com/paper/2303.02761/) (2023)
- [Scalable handwritten text recognition system for lexicographic sources of under-resourced languages and alphabets](https://mlsanity.com/paper/2303.16256/) (2023)
- [Align, Minimize and Diversify: A Source-Free Unsupervised Domain Adaptation Method for Handwritten Text Recognition](https://mlsanity.com/paper/2404.18260/) (2024)

## Abstract (arXiv)

We address the problem of predicting similarity between a pair of handwritten document images written by different individuals. This has applications related to matching and mining in image collections containing handwritten content. A similarity score is computed by detecting patterns of text re-usages between document images irrespective of the minor variations in word morphology, word ordering, layout and paraphrasing of the content. Our method does not depend on an accurate segmentation of words and lines. We formulate the document matching problem as a structured comparison of the word distributions across two document images. To match two word images, we propose a convolutional neural network (CNN) based feature descriptor. Performance of this representation surpasses the state-of-the-art on handwritten word spotting. Finally, we demonstrate the applicability of our method on a practical problem of matching handwritten assignments.