Text localization and recognition in complex scenes using local features

Authors:
Qi Zheng;Kai Chen;Yi Zhou;Congcong Gu;Haibing Guan
Affiliations:
School of Information Security Engineering, Shanghai Jiao Tong University;School of Information Security Engineering, Shanghai Jiao Tong University;School of Information Security Engineering, Shanghai Jiao Tong University;School of Information Security Engineering, Shanghai Jiao Tong University;Department of Computer Science and Engineering, Shanghai Jiao Tong University
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Year:
2010

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Abstract

We describe an approach using local features to resolve problems in text localization and recognition in complex scenes. Low image quality, complex background and variations of text make these problems challenging. Our approach includes the following stages: (1) Template images are generated automatically; (2) SIFT features are extracted and matched to template images; (3) Multiple single-character-areas are located using segmentation algorithm based upon multiple-size sliding subwindows; (4) An voting and geometric verification algorithm is used to identify final results. This framework thus is essentially simple by skipping many steps, such as normalization, binarization and OCR, which are required in previous methods. Moreover, this framework is robust as only SIFT feature is used. We evaluated our method using 200,000+ images in 3 scripts (Chinese, Japanese and Korean). We obtained average single-character success rate of 77.3% (highest 94.1%), average multiplecharacter success rate of 63.9% (highest 89.6%).