Fast Photo Time-Stamp Recognition Based on SGNN
Photo time-stamp is a valuable information source for content-based retrieval of scanned photo databases. A fast photo-stamp recognizing approach based on Self-Generating Neural Networks (SGNN) is proposed in this paper. Network structures and parameters of SGNN needn’t to be set by users, and their learning process needn’t iteration, so SGNN can be trained on-line. Proposed method consists of three steps: A photo is roughly segmented to determine which corner of the photo contains time-stamp; The area which contains time-stamp of the photo is finely segmented, in order to locate each character in the time-stamp, projection technology is used to locate edges of these characters; The time-stamp is recognized based on SGNN. Experimental results show that proposed approach can achieve higher recognition accuracy and computing efficiency.
Unable to display preview. Download preview PDF.
- Yin, P., Hua, X.S., Zhang, H.J.: Automatic Time Stamp Extraction System for Home Videos. In: Proc. of IEEE Int. Symp. on Circuits and Systems, pp. 73–76. IEEE, New York (2002)Google Scholar
- Chen, X., Zhang, H.: Photo Time-Stamp Detection and Recognition. In: Proc. of Int. Conf. on Document Analysis and Recognition, pp. 319–322. IEEE Computer Society, Los Alamitos (2003)Google Scholar
- Bao, F.M., Li, A.G., Qin, Z.: Photo Time-Stamp Recognition Based on Particle Swarm Optimization. In: Zhong, N., Tirri, H., Yao, Y., et al. (eds.) Proc. of IEEE/Wic/ACM Int. Conf. on Web Intelligence, pp. 529–532. IEEE Computer Society, Los Alamitos (2004)Google Scholar
- Wen, W.X., Jennings, A., Liu, H.: Learning A Neural Tree. In: Proc. of Int. Joint Conf. on Neural Networks, Beijing, China, pp. 751–756 (1992)Google Scholar