Exploiting colour information for better scene text detection and recognition
This paper presents an approach for text detection and recognition in scene images. The main contribution of this paper is to demonstrate that the colour information within the images if efficiently exploited is good enough to identify text regions from the surrounding noise. In the same way, the colour information present in character and word images can be used to achieve significant performance improvement in the recognition of characters and words. The proposed pipeline makes use of the colour information and low-level image processing operations to enhance text information that improves the overall performance of text detection and recognition in the wild. The proposed method offers two main advantages. First, it enhances the text regions up to a level of clarity where a simple off-the-shelf feature representation and classification method achieves state-of-the-art recognition performance. Second, the proposed framework is computationally fast as compared to other text detection and recognition techniques that offer good accuracy at the cost of significantly high latency. We performed extensive experimentation to evaluate our method on challenging benchmark datasets (Chars74K, ICDAR03, ICDAR11 and SVT), and the results show a considerable performance improvement.
character recognition word recognition colour constancy bilateral regression histogram of oriented gradients support vector machine