This paper proposes a new algorithm to provide high quality potential trademark locations for trademark detection. In real-world circumstances, trademark regions often possess some distinctive, invariant and stable properties which can be gained effectively and efficiently by Maximally Stable Extremal Regions (MSERs). Based on this observation, we design Trademark Confidence Score (TCS) for adaptive MSERs in the images. Then a window refinement algorithm is proposed to retain the high-quality candidate windows generated by Selective Search (SS). Experiments on FlickerLogos-27 and our own dataset demonstrate that our algorithm can significantly reduce the number of candidate proposals produced by SS with little sacrifice of recall for trademarks. Moreover, for trademark detection, our algorithm has better performance while reducing the computational cost of detection.
The whole process of text detection in scene images always contain three steps: character candidate detection, false character candidate removal, words extraction. However some errors appear in each step and influence the performance of text detection. According to the disadvantages of each step, we propose the compensation methods to solve these problems. Firstly, a filter based on color of stroke named Stroke Color Transform is used to ensure the integrality of characters and remove some false character candidates. Secondly, a classifier is trained based on gradient features is adopted to remove false character candidates. Thirdly, an extractor based on color of consecutive character named Character Color Transform is employed to extract undetected characters. The proposed technique is test on the two public datasets i.e. ICDAR2011 dataset, ICDAR2013 dataset, the experimental results show that our approach outperforms the state-of-the-art methods.
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