In this paper, we propose a structure learning-based scene character detector which is inspired by the observation that characters have their own inherent structures compared with the background. Graphs are extracted from the thinned binary image to represent the topological line structures of scene contents. Then, a graph classifier, namely gBoost classifier, is trained with the intent to seek out the inherent structures of character and the counterparts of non-character. The experimental results show that the proposed detector achieves the remarkable classification performance with the accuracy of about 70%, which demonstrates the existence and separability of the inherent structures.
|Number of pages||5|
|Journal||Proceedings of the International Conference on Document Analysis and Recognition, ICDAR|
|Publication status||Published - 2013|
|Event||12th International Conference on Document Analysis and Recognition, ICDAR 2013 - Washington, DC, United States|
Duration: Aug 25 2013 → Aug 28 2013
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition