Automatic unsupervised parameter selection for character segmentation

A major difficulty for designing a document image segmentation methodology is the proper value selection for all involved parameters. This is usually done after experimentations or after involving a training supervised phase which is a tedious process since the corresponding segmentation ground truth has to be created. In this paper, we propose a novel automatic unsupervised parameter selection methodology that can be applied to the character segmentation problem. It is based on clustering of the entities obtained as a result of the segmentation for different values of the parameters involved in the segmentation method. The clustering is performed using features extracted from the segmented entities based on zones and from the area that is formed from the projections of the upper/lower and left/right profiles. Optimization of an appropriate intra-class distance measure yields the optimal parameter vector. The method is evaluated on two segmentation algorithms, namely a recently propos...

• Type: Journal
• Year: 2010
• Where: DAS
• Authors: Georgios Vamvakas, Nikolaos Stamatopoulos, Basilios Gatos, Stavros J. Perantonis
• Keywords: Character Segmentation, DAS 2010, Document Analysis, Parameter, Parameter Selection