We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition. Many existing methods rely on Histogram of Oriented Gradient (HOG) or part-based models, which do not span the feature space well for characters in natural scene images, especially given large variation in fonts with cluttered backgrounds.
In this work, we propose a discriminative feature pooling method that automatically learns the most informative sub-regions of each scene character within a multi-class classification framework, whereas each sub-region seamlessly integrates a set of low-level image features through integral images.
The proposed feature representation is compact, computationally efficient, and able to effectively model distinctive spatial structures of each individual character class. Extensive experiments conducted on challenging datasets (Chars74K, ICDAR’03, ICDAR’11, SVT) show that our method significantly outperforms existing methods on scene character classification and scene text recognition tasks.
xplosion of mobile devices with cameras, online search has moved beyond text to other modalities like images, voice, and writing. For many applications like Fashion, image-based search offers a compelling interface as compared to text forms by better capturing the visual attributes.
In this paper we present a simple and fast search algorithm that uses color as the main feature for building visual search. We show that low level cues such as color can be used to quantify image similarity and also to discriminate among products with different visual appearances.
We demonstrate the effectiveness of our approach through a mobile shopping application (eBay Fashion App available at https://itunes.apple.com/us/app/ebay-fashion/id378358380?mt=8 and eBay image swatch is the feature indexing millions of real world fashion images).
Our approach outperforms several other state-of-the-art image retrieval algorithms for large scale image data.