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 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%).
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References
Lowe, D.: Distinctive image features from scale-invariant keypoints. In: ICCV, vol. 2, pp. 91–110 (2004)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. In: CVPR, vol. 2, pp. 506–513 (2004)
Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. In: CVPR, vol. 2, pp. 257–263 (2003)
Tuytelaars, T., Mikolajczyk, K.: A Survey on Local Invariant Features. Foundations and Trends in Computer Graphics and Vision (2008)
Chen, X., Yuille, A.: Detecting and Reading Text in Natural Scenes. In: CVPR, vol. 2, pp. 366–373 (2004)
Chen, X., Yang, J., Zhang, J., Waibel, A.: Automatic detection and recognition of signs from natural scenes. IEEE Transactions on Image Processing 13, 87–99 (2004)
Chang, S.L., Chen, L.S., Chung, Y.C., Chen, S.W.: Automatic License Plate Recognition. IEEE Transactions on Intelligent Transportation Systems 5, 42–53 (2004)
Koga, M., Mine, R., Kameyama, T., Takahashi, T., Yamazaki, M., Yamaguchi, T.: Camera-based Kanji OCR for mobile-phones: practical issues. In: ICDAR (2005)
Liang, J., Doermann, D., Li, H.: Camera-based analysis of text and documents: A survey. IJDAR 7, 84–104 (2005)
Jung, K., Kim, K.I., Jain, A.K.: Text information extraction in images and video: a survey. Pattern Recognition 37, 977–997 (2004)
Fujisawa, H.: Forty years of research in character and document recognition - an industrial perspective. Pattern Recognition 41, 2435–2446 (2008)
de Campos, T.E., Babu, B.R., Varma, M.: Character recognition in natural images. In: VISAPP (2009)
Lv, Q., Josephson, W., Wang, Z., Charikar, M., Li, K.: Multi-probe LSH: Efficient indexing for high-dimensional similarity search. In: VLDB, pp. 950–961 (2007)
Johnson, D.S.: Approximation algorithms for combinational problems. JCSS 9, 256–278 (1974)
Leordeanu, M., Hebert, M.: A Spectral Technique for Correspondence Problems Using Pairwise Constraints. In: ICCV, vol. 2, pp. 1482–1489 (2005)
Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: CVPR, pp. 511–218 (2001)
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Zheng, Q., Chen, K., Zhou, Y., Gu, C., Guan, H. (2011). Text Localization and Recognition in Complex Scenes Using Local Features. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_10
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DOI: https://doi.org/10.1007/978-3-642-19318-7_10
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