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A hierarchical recursive method for text detection in natural scene images

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Abstract

Text detection in natural scene images is a challenging problem in computer vision. To robust detect various texts in complex scenes, a hierarchical recursive text detection method is proposed in this paper. Usually, texts in natural scenes are not alone and arranged into lines for easy reading. To find all possible text lines in an image, candidate text lines are obtained using text edge box and conventional neural network at first. Then, to accurately find out the true text lines in the image, these candidate text lines are analyzed in a hierarchical recursive architecture. For each of them, connected components segmentation and hierarchical random field based analysis are recursively employed until the detected text line no more changes. Now the detected text lines are output as the text detection result. Experiments on ICDAR 2003 dataset, ICDAR 2013 dataset and Street View Dataset show that the hierarchical recursive architecture can improve text detection performance and the proposed method achieves the state-of-art in scene text detection.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (91520301).

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Authors and Affiliations

  1. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, 710049, China

    Xiaobing Wang, Yonghong Song, Yuanlin Zhang & Jingmin Xin

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  1. Xiaobing Wang
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  2. Yonghong Song
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  3. Yuanlin Zhang
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  4. Jingmin Xin
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Correspondence to Yonghong Song.

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Wang, X., Song, Y., Zhang, Y. et al. A hierarchical recursive method for text detection in natural scene images. Multimed Tools Appl 76, 26201–26223 (2017). https://doi.org/10.1007/s11042-016-4099-2

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