Paper
22 December 1999 Benchmarking of document page segmentation
Stefan Agne, Markus Rogger, Joerg Rohrschneider
Author Affiliations +
Proceedings Volume 3967, Document Recognition and Retrieval VII; (1999) https://doi.org/10.1117/12.373490
Event: Electronic Imaging, 2000, San Jose, CA, United States
Abstract
The decomposition of a document into segments such as text regions and graphics is a significant part of the document analysis process. The basic requirement for rating and improvement of page segmentation algorithms is systematic evaluation. The approaches known from the literature have the disadvantage that manually generated reference data (zoning ground truth) are needed for the evaluation task. The effort and cost of the creation of these data are very high. This paper describes the evaluation system SEE. The system requires the OCR generated text and the original text of the document in correct reading order (text ground truth) as input. No manually generated zoning ground truth is needed. The implicit structure information that is contained in the text ground truth is used for the evaluation of the automatic zoning. Therefore, an assignment of the corresponding text regions in the text ground truth and those in the OCR generated text (matches) is sought. A fault tolerant string matching algorithm is used to develop a method which tolerates OCR errors in the text. The segmentation errors are determined as a result of the evaluation of the matching. Subsequently, the edit operations which are necessary for the correction of the recognized segmentation errors are computed to estimate the correction costs.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stefan Agne, Markus Rogger, and Joerg Rohrschneider "Benchmarking of document page segmentation", Proc. SPIE 3967, Document Recognition and Retrieval VII, (22 December 1999); https://doi.org/10.1117/12.373490
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Cited by 9 scholarly publications.
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KEYWORDS
Image segmentation

Optical character recognition

Error analysis

Raster graphics

Algorithm development

Visualization

Detection and tracking algorithms

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