# Deep Learning for Logo Recognition

## TL;DR

This paper introduces a deep learning-based logo recognition system that combines region proposal and CNN classification, demonstrating superior performance on the FlickrLogos-32 dataset through extensive experiments.

## Contribution

It presents a novel logo recognition pipeline integrating region proposal and CNN classification, with systematic analysis of training strategies and data augmentation effects.

## Key findings

- Outperforms state-of-the-art methods on FlickrLogos-32
- Synthetic data augmentation improves recognition accuracy
- Explicit background modeling enhances classification robustness

## Abstract

In this paper we propose a method for logo recognition using deep learning. Our recognition pipeline is composed of a logo region proposal followed by a Convolutional Neural Network (CNN) specifically trained for logo classification, even if they are not precisely localized. Experiments are carried out on the FlickrLogos-32 database, and we evaluate the effect on recognition performance of synthetic versus real data augmentation, and image pre-processing. Moreover, we systematically investigate the benefits of different training choices such as class-balancing, sample-weighting and explicit modeling the background class (i.e. no-logo regions). Experimental results confirm the feasibility of the proposed method, that outperforms the methods in the state of the art.