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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Text Localization and Recognition in Complex Scenes Using Local Features
verfasst von : Qi Zheng, Kai Chen, Yi Zhou, Congcong Gu, Haibing Guan
Erschienen in: Computer Vision – ACCV 2010
Verlag: Springer Berlin Heidelberg
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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 sub-windows; (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 multiple-character success rate of 63.9% (highest 89.6%).