A Robust Approach for Recognition of Text Embedded in Natural Scenes Export

@inproceedings{citeulike:5789760, abstract = {In this paper, we propose a robust approach for recognition of text embedded in natural scenes. Instead of using binary information as most other OCR systems do, we extract features from intensity of an image directly. We utilize a local intensity normalization method to effectively handle lighting variations. We then employ Gabor transform to obtain local features, and use LDA for selection and classification of features. The proposed method has been applied to a Chinese sign recognition task. The system can recognize a vocabulary of 3755 Level 1 Chinese characters in the Chinese national standard character set GB2312-80 with various print fonts. We tested the system on 1630 test characters in sign images captured from the natural scenes, and the recognition accuracy is 92.46\%. We have already integrated the system into our automatic Chinese sign translation system.}, address = {Washington, DC, USA}, author = {Zhang, Jing and Hanneman, Andreas and Yang, Jie and Waibel, Alex}, booktitle = {ICPR '02: Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3}, citeulike-article-id = {5789760}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=842887}, comment = {Gabor wavelet based approach for Chinese sign recognition. 92\% accuracy}, isbn = {0-7695-1695-X}, keywords = {ocr}, pages = {30204+}, posted-at = {2009-09-15 23:51:05}, priority = {2}, publisher = {IEEE Computer Society}, title = {A Robust Approach for Recognition of Text Embedded in Natural Scenes}, url = {http://portal.acm.org/citation.cfm?id=842887}, year = {2002} }

TY - CONF ID - citeulike:5789760 L3 - citeulike-article-id:5789760 N2 - In this paper, we propose a robust approach for recognition of text embedded in natural scenes. Instead of using binary information as most other OCR systems do, we extract features from intensity of an image directly. We utilize a local intensity normalization method to effectively handle lighting variations. We then employ Gabor transform to obtain local features, and use LDA for selection and classification of features. The proposed method has been applied to a Chinese sign recognition task. The system can recognize a vocabulary of 3755 Level 1 Chinese characters in the Chinese national standard character set GB2312-80 with various print fonts. We tested the system on 1630 test characters in sign images captured from the natural scenes, and the recognition accuracy is 92.46%. We have already integrated the system into our automatic Chinese sign translation system. SN - 0-7695-1695-X AD - Washington, DC, USA TI - A Robust Approach for Recognition of Text Embedded in Natural Scenes PB - IEEE Computer Society T2 - ICPR '02: Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 SP - 30204 KW - ocr N1 - Gabor wavelet based approach for Chinese sign recognition. 92% accuracy AU - Zhang, Jing AU - Hanneman, Andreas AU - Yang, Jie AU - Waibel, Alex PY - 2002/// UR - http://portal.acm.org/citation.cfm?id=842887 ER -