Abstract
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.
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Notes
- 1.
There are several variations in the logo type classification. For example, in [1], three types are called “text-based logo”, “iconic or symbolic logo”, and “mixed logo”, respectively.
- 2.
Not available now. According to [15], it contained 123 logo images.
- 3.
The number of followers is provided in the file LLD-logo.hdf5, which is provided with LLD-logo image data. Precisely, the resource of meta_data/twitter/user_objects in this hdf5 file contains the followers_count data, which corresponds to the number of followers.
- 4.
For the logarithmic plot, we exclude the companies with zero follower. The number of such companies is around 1,000 and thus no serious effect in our discussion.
- 5.
As we will see later, the text area ratio sometimes exceeds 100%. There are several reasons behind it; one major reason is the ambiguity of the whole logo’s bounding box and the individual text areas’ bounding boxes. If the latter slightly becomes the former, the ratio exceeds 100%. Some elaborated post-processing might reduce those cases; however, they do not significantly affect our median-based analysis.
- 6.
Recall that each distribution is normalized. As noted before, logotypes are just 4%, whereas logo symbols are 26%. Thus, as the absolute numbers, the famous companies use more logo symbols than logotypes.
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Acknowledgment
This work was supported by JSPS KAKENHI Grant Number JP17H06100.
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Nishi, S., Kadota, T., Uchida, S. (2021). Famous Companies Use More Letters in Logo: A Large-Scale Analysis of Text Area in Logo. In: Barney Smith, E.H., Pal, U. (eds) Document Analysis and Recognition – ICDAR 2021 Workshops. ICDAR 2021. Lecture Notes in Computer Science(), vol 12916. Springer, Cham. https://doi.org/10.1007/978-3-030-86198-8_8
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