As an important information carrier, texts play significant roles in many applications. However, text detection in unconstrained scenes is a challenging problem due to cluttered backgrounds, various appearances, uneven illumination, etc.. In this paper, an approach based on multi-channel information and local context is proposed to detect texts in natural scenes. According to character candidate detection plays a vital role in text detection system, Maximally Stable Extremal Regions(MSERs) and Graph-cut based method are integrated to obtain the character candidates by leveraging the multi-channel image information. A cascaded false positive elimination mechanism are constructed from the perspective of the character and the text line respectively. Since the local context information is very valuable for us, these information is utilized to retrieve the missing characters for boosting the text detection performance. Experimental results on two benchmark datasets, i.e., the ICDAR 2011 dataset and the ICDAR 2013 dataset, demonstrate that the proposed method have achieved the state-of-the-art performance.
In this paper, a hybrid approach is proposed to detect texts in natural scenes. It is performed by the following steps:
Firstly, the edge map and the text saliency region are obtained. Secondly, the text candidate regions are detected by
connected components (CC) based method and are identified by an off-line trained HOG classifier. And then, the
remaining CCs are grouped into text lines with some heuristic strategies to make up for the false negatives. Finally, the
text lines are broken into separate words. The performance of the proposed approach is evaluated on the location
detection database of ICDAR 2003 robust reading competition. Experimental results demonstrate the validity of our
approach and are competitive with other state-of-the-art algorithms.