A novel binarization approach for text in images

Accurate recognition of scene text and overlaid text is still a challenging issue due to degradation and complex background, and text binarization is crucial for recognition accuracy. This paper presents an effective method to extract characters in images and video frames. Our method assumes that background pixels possess good spatial connectivity and high appearance similarity to boundary pixels in a cropped text string image. It first computes the confidence of pixels as text. Then the confidence map is exploited to partition text regions into characters. Further, each character region is clustered into different layers and background components are removed to generate candidate binarization results. The final result is obtained based on the scores of each layer. Our method is validated by better recognition rates and segmentation accuracy on the ICADR03 dataset and a big dataset of overlaid text.