Document Logo Detection and Recognition Using Bayesian Model

This paper presents a simple, dynamic approach to logo detection and recognition in document images. Although there are literatures on both logo detection and logo recognition issues, Current methods lack the adaptability to variable real-world documents. In this paper we initially observe this deficiency from a different point of view and reveal its inherent causation. Then we reorganize the structure of the logo detection and recognition procedures and integrate them into a unified framework. By applying feedback and selecting proper features, we make our framework dynamic and interactive. Experiments show that the proposed method outperforms existing methods in document processing domain.

[1]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[2]  Shlomo Argamon,et al.  Building a test collection for complex document information processing , 2006, SIGIR.

[3]  Youbin Chen,et al.  Logo Detection in Document Images Based on Boundary Extension of Feature Rectangles , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[4]  David G. Stork,et al.  Pattern Classification , 1973 .

[5]  Jingying Chen,et al.  Noisy logo recognition using line segment Hausdorff distance , 2003, Pattern Recognit..

[6]  David Doermann,et al.  Automatic Document Logo Detection , 2007 .

[7]  Yves Lecourtier,et al.  Symbol Detection Using Region Adjacency Graphs and Integer Linear Programming , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[8]  Ehud Rivlin,et al.  Logo recognition using geometric invariants , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).