Robust Text Segmentation in Low Quality Images via Adaptive Stroke Width Estimation and Stroke Based Superpixel Grouping

Text segmentation is an important step in the process of character recognition. In literature, there have been numerous methods that work very well in practical applications. However, when an image includes strong noise or surface reflection distraction, accurate text segmentation still faces many challenges. Observing that the stroke width of text is stable and significantly different from that of reflective regions generally, we present a novel method for text segmentation using adaptive stroke width estimation and simple linear iterative clustering superpixel (SLIC-superpixel) region growing in this paper. It consists of four following steps: The first is to normalize image intensity to overcome the influence of gray changes. The second utilizes the intensity consistency to compute normalized stroke width (NSW) map. The third is to estimate the optimal stroke width through searching for the peak value of the histogram of normalized stroke width, the text polarity is also determined. Finally, we propose a local region growing method for text extraction using SLIC-superpixel. Unlike current existing methods of computing stroke width, such as gray level jump on a horizontal scan line and gradient-based SWT methods, the proposed method is based on the statistics of stroke width in the whole image. Hence the stroke width estimation is not only invariant in scale and rotation, but also more robust to surface reflection and noise than that of those methods based only on the pairs of sudden changes of intensity or gradient maps. Experiments with many real images, such as laser marking detonator codes, notice signatures and vehicle license plates, etc., have shown that the proposed algorithm can work well in noised images and also achieve comparable performance with current state-of-the-art method on text segmentation from low quality images.