Explicit foreground and background modeling in the classification of text blocks in scene images

Achieving high accuracy for classifying foreground and background is an interesting challenge in the field of scene image analysis because of the wide range of illumination, complex background, and scale changes. Classifying foreground and background using bag-of-feature model gives a good result. However, the performance of the classifier depends on designed features. Therefore, this paper presents an alternative classification method based on three categories of object-attributes features namely object description, color distribution and gradient strength. Each feature is computed to a classifier model. The robustness of the method has been tested on the ICDAR2015 dataset. The experimental results show that the performance of the proposed method performs competitively against the results of existing methods in term of precision and recall.