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Trademark retrieval (TR) has become an important yet challenging problem dueto an ever increasing trend in trademark applications and infringementincidents. There have been many promising attempts for the TR problem, which,however, fell impracticable since they were evaluated with limited and mostlytrivial datasets. In this paper, we provide a large-scale dataset withbenchmark queries with which different TR approaches can be evaluatedsystematically. Moreover, we provide a baseline on this benchmark using thewidely-used methods applied to TR in the literature. Furthermore, we identifyand correct two important issues in TR approaches that were not addressedbefore: reversal of contrast, and presence of irrelevant text in trademarksseverely affect the TR methods. Lastly, we applied deep learning, namely,several popular Convolutional Neural Network models, to the TR problem. To thebest of the authors, this is the first attempt to do so.

A Large-scale Dataset and Benchmark for Similar Trademark Retrieval