# Famous Companies Use More Letters in Logo:A Large-Scale Analysis of Text Area in Logo

arXiv: https://arxiv.org/abs/2104.00327 · HTML: https://mlsanity.com/paper/2104.00327/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering](https://mlsanity.com/paper/2003.08607/) (2020)
- [Online Deep Clustering for Unsupervised Representation Learning](https://mlsanity.com/paper/2006.10645/) (2020)
- [A Deep Look into Neural Ranking Models for Information Retrieval](https://mlsanity.com/paper/1903.06902/) (2019)
- [Clustering with Deep Learning: Taxonomy and New Methods](https://mlsanity.com/paper/1801.07648/) (2018)
- [Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification](https://mlsanity.com/paper/1911.07924/) (2019)
- [Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks](https://mlsanity.com/paper/1712.04407/) (2017)
- [An Universal Image Attractiveness Ranking Framework](https://mlsanity.com/paper/1805.00309/) (2018)

## Abstract (arXiv)

This paper analyzes a large number of logo images from the LLD-logo dataset, by recent deep learning-based techniques, to understand not only design trends of logo images and but also the correlation to their owner company. Especially, we focus on three correlations between logo images and their text areas, between the text areas and the number of followers on Twitter, and between the logo images and the number of followers. Various findings include the weak positive correlation between the text area ratio and the number of followers of the company. In addition, deep regression and deep ranking methods can catch correlations between the logo images and the number of followers.