Scale based region growing for scene text detection

  • Authors:

  • Junhua Mao;Houqiang Li;Wengang Zhou;Shuicheng Yan;Qi Tian

  • Affiliations:

  • University of Science and Technology of China, Hefei, China;University of Science and Technology of China, Hefei, China;University of Texas at San Antonio, San Antonio, TX, USA;National University of Singapore, Singapore, Singapore;University of Texas at San Antonio, San Antonio, TX, USA

  • Venue:
  • Proceedings of the 21st ACM international conference on Multimedia
  • Year:
  • 2013

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Abstract

Scene text is widely observed in our daily life and has many important multimedia applications. Unlike document text, scene text usually exhibits large variations in font and language, and suffers from low resolution, occlusions and complex background. In this paper, we present a novel scale-based region growing algorithm for scene text detection. We first distinguish SIFT features in text regions from those in background by exploring the inter- and intra-statistics of SIFT features. Then scene text regions in images are identified by scale-based region growing, which explores the geometric context of SIFT keypoints in local regions. Our algorithm is very effective to detect multilingual text in various fonts, sizes, and with complex background. In addition, it offers insights on efficiently deploying local features in numerous applications, such as visual search. We evaluate our algorithm on three datasets and achieve the state-of-the-art performance.