Document page segmentation is a crucial preprocessing step in Optical Character Recognition (OCR) system. While numerous segmentation algorithms have been proposed, there is relatively less literature on comparative evaluation -- empirical or theoretical -- of these algorithms. We use the following five step methodology to quantitatively compare the performance of page segmentation algorithms: (1) First we create mutually exclusive training and test dataset with groundtruth, (2) we then select a meaningful and computable performance metric, (3) an optimization procedure is then used to automatically search for the optimal parameter values of the segmentation algorithms, (4) the segmentation algorithms are then evaluated on the test dataset, and finally (5) a statistical error analysis is performed to give the statistical significance of the experimental results. We apply this methodology to five segmentation algorithms, three of which are representative research algorithms and the rest two are well-known commercial products. The three research algorithms evaluated are: Nagy's X-Y cut, O'Gorman's Docstrum and Kise's Voronoi-diagram-based algorithm. The two commercial products evaluated are: Caere Corporation's segmentation algorithm and ScanSoft Corporation's segmentation algorithm. The evaluations are conducted on 978 images from the University of Washington III dataset. It is found that the performance of the Voronoi-based, Docstrum and Caere's segmentation algorithms are not significantly different from each other, but they are significantly better than ScanSoft's segmentation algorithm, which in turn is significantly better than the performance of the X-Y cut algorithm. Furthermore, we see that the commercial segmentation algorithms and research segmentation algorithms have comparable performances.

Date Published: 22 December 1999
PDF: 12 pages
Proc. SPIE 3967, Document Recognition and Retrieval VII, (22 December 1999); doi: 10.1117/12.373507

Published in SPIE Proceedings Vol. 3967:
Document Recognition and Retrieval VII
Daniel P. Lopresti; Jiangying Zhou, Editor(s)