Performance Evaluation and Benchmarking of Six-Page Segmentation Algorithms

Faisal Shafait, Daniel Keysers, Thomas Breuel

In: IEEE Transactions on Pattern Analysis and Machine Intelligence 30 6 Pages 941-954 6/2008.


Informative benchmarks are crucial for optimizing the page segmentation step of an OCR system, frequently the performance limiting step for overall OCR system performance. We show that current evaluation scores are insufficient for diagnosing specific errors in page segmentation and fail to identify some classes of serious segmentation errors altogether. This paper introduces a vectorial score that is sensitive to, and identifies, the most important classes of segmentation errors (over-, under-, and miss segmentation) and what page components (lines, blocks, etc.) are affected. Unlike previous schemes, our evaluation method has a canonical representation of ground truth data and guarantees pixel-accurate evaluation results for arbitrary region shapes. We present the results of evaluating widely used seg mentation algorithms (x-y cut, smearing, whitespace analysis, constrained text-line finding, docstrum, and Voronoi) on the UW-III database and demonstrate that the new evaluation scheme permits the identification of several specific flaws in individual segmentation methods.


German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz