Recognizing perspective scene text with context feature

Text recognition has gained significant attention from the computer vision community. Correct character recognition is the premise of text recognition and affects the overall performance to large extent. This paper proposes a novel character representation for scene text recognition. First, a context-based feature that contains local information and relevant key points´ feature is extracted from key points. The relativity is measured by the distance of vector that is generated by a trained Gaussian Mixture Model (GMM) between the target key point and other key points in each context bin. In order to recognize each individual character, we adopt a bag-of-words approach, in which the rotation-invariant context features are densely extracted from an individual character. All key points´ context features are prone to build a vocabulary of visual words by using k-means clustering. Then we train a set of two-class linear Support Vector Machines in a one-vs-all schema for each category character. By using densely extracted context features that are rotation-invariant and efficient, our method is capable of recognizing perspective texts of arbitrary orientations. The evaluation results on benchmark datasets demonstrate that our proposed scheme of scene character recognition is highly efficient and achieves state-of-the-art performance on not only fontal character recognition but also perspective characters´.