Analysis of Segmentation Performance on the CEDAR Benchmark Database

The purpose of this paper is to analyse the performance of our improved segmentation algorithm tested on the CEDAR benchmark database. Segmentation is achieved through the extraction of a wide range of information adjacent to or surrounding suspicious segmentation points. Initially, a heuristic technique is employed to search for structural features and to over-segment each word. For each segmentation point that is located, the left character (preceding the segmentation point), and centre character (centred on the segmentation point) are extracted along with other features from the segmentation area. The aforementioned features are presented to trained character and segmentation point validation neural networks to evaluate a number of confidence values. Finally, the confidence values are fused to obtain the final segmentation decision. Based on a detailed analysis, it was observed that the left and centre character networks increased the accuracy of the segmentation algorithm.